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Mobile communications have become necessities and even everyday commodities. In the previous decades they evolved from a technology reserved for a limited number of individuals to the current systems that are everywhere in our lives. The task of developing mobile technologies has also evolved from a national or regional focus to a complex and growing mission, supported by global standards development organizations such as 3GPP (3rd Group Partnership Project). Thanks to the success of smart phones and mobile-ready laptops, data traffic has recently grown exponentially, and the demand for mobile data has risen very dramatically. These requests in large capacity can only be satisfied by a high efficiency and a very good optimization of the infrastructures of the mobile networks, while taking into account the constraints which are the power, bandwidth and a limited complexity. Through this research, we present everything related to the simulation of the 4G mobile network system (LTE), which can provide high data flow with good quality, through three model channels known as (EPA, EVA, ETU). In this work we focus on the block ‘iterative decoding channel encoder’ in the LTE system, where the iterative channel coding called ‘Turbo-code’ (TC) is substituted by the iterative coding channel called ‘Unpunctured Turbo Trellis-coded Modulation’ (UTTCM). The simulation results showed that with less decoding complexities, UTTCM's LTE system gives good performance (in terms of BER).

The purpose of this research was to explore the effect of the number and of function Chinese character radical on the EEG activity during the character updating paradigm by comparing the temporal processing patterns of neural activity in the N-back task.

This report presents the most recent results of an investigation into the complexity of decoding error-correcting codes and the development of efficient and practical decoding techniques. The most important result has been the discovery of a new general mathematical framework in which digital decoding and analog demodulation become special cases of a more general class of decoding-demodulation functions. Although we have only just begun to explore the many possibilities opened up by this discovery, results of practical importance have already been produced, including an optimum soft-decision symbol-by-symbol decoding algorithm for linear codes whose complexity varies inversely with code rate. (Author)

This article is from Sensors (Basel, Switzerland) , volume 12 . Abstract Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

Polar codes under cyclic redundancy check aided successive cancellation list (CA-SCL) decoding can outperform the turbo codes and the LDPC codes when code lengths are configured to be several kilobits. In order to reduce the decoding complexity, a novel tree-pruning scheme for the \mbox{SCL/CA-SCL} decoding algorithms is proposed in this paper. In each step of the decoding procedure, the candidate paths with metrics less than a threshold are dropped directly to avoid the unnecessary computations for the path searching on the descendant branches of them. Given a candidate path, an upper bound of the path metric of its descendants is proposed to determined whether the pruning of this candidate path would affect frame error rate (FER) performance. By utilizing this upper bounding technique and introducing a dynamic threshold, the proposed scheme deletes the redundant candidate paths as many as possible while keeping the performance deterioration in a tolerant region, thus it is much more efficient than the existing pruning scheme. With only a negligible loss of FER performance, the computational complexity of the proposed pruned decoding scheme is only about $40\%$ of the standard algorithm in the low signal-to-noise ratio (SNR) region (where the FER under CA-SCL decoding is about $0.1 \sim 0.001$), and it can be very close to that of the successive cancellation (SC) decoder in the moderate and high SNR regions.

We consider the problem of finding a trellis for a linear block code that minimizes one or more measures of trellis complexity. The domain of optimization may be different permutations of the same code or different codes with the same parameters. Constraints on trellises, including relationships between the minimal trellis of a code and that of the dual code, are used to derive bounds on complexity. We define a partial ordering on trellises: If a trellis is optimum with respect to this partial ordering, if has the desirable property that it simultaneously minimizes all of the complexity measures examined. We examine properties of such optimal trellises and give examples of optimal permutations of codes, most notably the (48,24,12) quadratic residue code.

This paper proposes a low decoding complexity, full-diversity and full-rate space-time block code (STBC) for 4 transmit and 2 receive ($4\times 2$) multiple-input multiple-output (MIMO) systems. For such systems, the best code known is the DjABBA code and recently, Biglieri, Hong and Viterbo have proposed another STBC (BHV code) which has lower decoding complexity than DjABBA but does not have full-diversity like the DjABBA code. The code proposed in this paper has the same decoding complexity as the BHV code for square QAM constellations but has full-diversity as well. Compared to the best code in the DjABBA family of codes, our code has lower decoding complexity, a better coding gain and hence a better error performance as well. Simulation results confirming these are presented.

This document describes an RTP payload format for the Reduced- Complexity Decoding Operation (RCDO) for H.264 Baseline profile bitstreams, as specified in ITU-T Recommendation H.241. RCDO reduces the decoding cost and resource consumption of the video processing. The RCDO RTP payload format is based on the H.264 RTP payload format. [STANDARDS-TRACK]

We consider transmission over a general memoryless channel, with bounded decoding complexity per bit under message passing decoding. We show that the achievable rate is bounded below capacity if there is a finite success in the decoding in a specified number of operations per bit at the decoder for some codes on graphs. These codes include LDPC and LDGM codes. Good performance with low decoding complexity suggests strong local structures in the graphs of these codes, which are detrimental to the code rate asymptotically. The proof method leads to an interesting necessary condition on the code structures which could achieve capacity with bounded decoding complexity. We also show that if a code sequence achieves a rate epsilon close to the channel capacity, the decoding complexity scales at least as O(log(1/epsilon).

This work explores the rate-reliability-complexity limits of the quasi-static K-user multiple access channel (MAC), with or without feedback. Using high-SNR asymptotics, the work first derives bounds on the computational resources required to achieve near-optimal (ML-based) decoding performance. It then bounds the (reduced) complexity needed to achieve any (including suboptimal) diversity-multiplexing performance tradeoff (DMT) performance, and finally bounds the same complexity, in the presence of feedback-aided user selection. This latter effort reveals the ability of a few bits of feedback not only to improve performance, but also to reduce complexity. In this context, the analysis reveals the interesting finding that proper calibration of user selection can allow for near-optimal ML-based decoding, with complexity that need not scale exponentially in the total number of codeword bits. The derived bounds constitute the best known performance-vs-complexity behavior to date for ML-based MAC decoding, as well as a first exploration of the complexity-feedback-performance interdependencies in multiuser settings.

Recently Li and Xia have proposed a transmission scheme for wireless relay networks based on the Alamouti space time code and orthogonal frequency division multiplexing to combat the effect of timing errors at the relay nodes. This transmission scheme is amazingly simple and achieves a diversity order of two for any number of relays. Motivated by its simplicity, this scheme is extended to a more general transmission scheme that can achieve full cooperative diversity for any number of relays. The conditions on the distributed space time code (DSTC) structure that admit its application in the proposed transmission scheme are identified and it is pointed out that the recently proposed full diversity four group decodable DSTCs from precoded co-ordinate interleaved orthogonal designs and extended Clifford algebras satisfy these conditions. It is then shown how differential encoding at the source can be combined with the proposed transmission scheme to arrive at a new transmission scheme that can achieve full cooperative diversity in asynchronous wireless relay networks with no channel information and also no timing error knowledge at the destination node. Finally, four group decodable distributed differential space time codes applicable in this new transmission scheme for power of two number of relays are also provided.

In this paper, the asymptotic performance of the lattice sequential decoder for LAttice Space-Time (LAST) coded MIMO channel is analyzed. We determine the rates achievable by lattice coding and sequential decoding applied to such a channel. The diversity-multiplexing tradeoff (DMT) under lattice sequential decoding is derived as a function of its parameter---the bias term, which is critical for controlling the amount of computations required at the decoding stage. Achieving low decoding complexity requires increasing the value of the bias term. However, this is done at the expense of losing the optimal tradeoff of the channel. In this work, we derive the tail distribution of the decoder's computational complexity in the high signal-to-noise ratio regime. Our analysis reveals that the tail distribution of such a low complexity decoder is dominated by the outage probability of the channel for the underlying coding scheme. Also, the tail exponent of the complexity distribution is shown to be equivalent to the DMT achieved by lattice coding and lattice sequential decoding schemes. We derive the asymptotic average complexity of the sequential decoder as a function of the system parameters. In particular, we show that there exists a cut-off multiplexing gain for which the average computational complexity of the decoder remains bounded.

In this work we show how to decompose a linear code relatively to any given poset metric. We prove that the complexity of syndrome decoding is determined by a maximal (primary) such decomposition and then show that a refinement of a partial order leads to a refinement of the primary decomposition. Using this and considering already known results about hierarchical posets, we can establish upper and lower bounds for the complexity of syndrome decoding relatively to a poset metric.

The problem of designing high rate, full diversity noncoherent space-time block codes (STBCs) with low encoding and decoding complexity is addressed. First, the notion of $g$-group encodable and $g$-group decodable linear STBCs is introduced. Then for a known class of rate-1 linear designs, an explicit construction of fully-diverse signal sets that lead to four-group encodable and four-group decodable differential scaled unitary STBCs for any power of two number of antennas is provided. Previous works on differential STBCs either sacrifice decoding complexity for higher rate or sacrifice rate for lower decoding complexity.

The complexity of maximal likelihood decoding of the Reed-Solomon codes $[q-1, k]_q$ is a well known open problem. The only known result in this direction states that it is at least as hard as the discrete logarithm in some cases where the information rate unfortunately goes to zero. In this paper, we remove the rate restriction and prove that the same complexity result holds for any positive information rate. In particular, this resolves an open problem left in [4], and rules out the possibility of a polynomial time algorithm for maximal likelihood decoding problem of Reed-Solomon codes of any rate under a well known cryptographical hardness assumption. As a side result, we give an explicit construction of Hamming balls of radius bounded away from the minimum distance, which contain exponentially many codewords for Reed-Solomon code of any positive rate less than one. The previous constructions only apply to Reed-Solomon codes of diminishing rates. We also give an explicit construction of Hamming balls of relative radius less than 1 which contain subexponentially many codewords for Reed-Solomon code of rate approaching one.

So far all the proven unconditionally secure prepare and measure protocols for the quantum key distribution(QKD) must solve the very complex problem of decoding the classical CSS code. In the decoding stage, Bob has to compare his string with an exponentially large number of all the strings in certain code space to find out the closest one. Here we have spotted that, in an entanglement purification protocol(EPP), the random basis in the state preparation stage is only necessary to those check qubits, but uncessary to the code qubits. In our modified two way communication EPP(2-EPP) protocol, Alice and Bob may first take all the parity checks on $Z$ basis to reduce the bit flip error to strictly zero with a high probability, e.g., $1-2^{-30}$, and then use the CSS code to obtain the final key. We show that, this type of 2-EPP protocol can be reduced to an equivalent prepare and measure protocol. In our protocol, the huge complexity of decoding the classical CSS code is totally removed.

We construct a class of linear space-time block codes for any number of transmit antennas that have controllable ML decoding complexity with a maximum rate of 1 symbol per channel use. The decoding complexity for $M$ transmit antennas can be varied from ML decoding of $2^{\lceil \log_2M \rceil -1}$ symbols together to single symbol ML decoding. For ML decoding of $2^{\lceil \log_2M \rceil - n}$ ($n=1,2,...$) symbols together, a diversity of $\min(M,2^{\lceil \log_2M \rceil-n+1})$ can be achieved. Numerical results show that the performance of the constructed code when $2^{\lceil \log_2M \rceil-1}$ symbols are decoded together is quite close to the performance of ideal rate-1 orthogonal codes (that are non-existent for more than 2 transmit antennas).

his study presents a novel technique to estimate the computational complexity of sequential decoding using the Berry-Esseen theorem. Unlike the theoretical bounds determined by the conventional central limit theorem argument, which often holds only for sufficiently large codeword length, the new bound obtained from the Berry-Esseen theorem is valid for any blocklength. The accuracy of the new bound is then examined for two sequential decoding algorithms, an ordering-free variant of the generalized Dijkstra's algorithm (GDA)(or simplified GDA) and the maximum-likelihood sequential decoding algorithm (MLSDA). Empirically investigating codes of small blocklength reveals that the theoretical upper bound for the simplified GDA almost matches the simulation results as the signal-to-noise ratio (SNR) per information bit ($\gamma_b$) is greater than or equal to 8 dB. However, the theoretical bound may become markedly higher than the simulated average complexity when $\gamma_b$ is small. For the MLSDA, the theoretical upper bound is quite close to the simulation results for both high SNR ($\gamma_b\geq 6$ dB) and low SNR ($\gamma_b\leq 2$ dB). Even for moderate SNR, the simulation results and the theoretical bound differ by at most \makeblue{0.8} on a $\log_{10}$ scale.

For the majority of the applications of Reed-Solomon (RS) codes, hard decision decoding is based on syndromes. Recently, there has been renewed interest in decoding RS codes without using syndromes. In this paper, we investigate the complexity of syndromeless decoding for RS codes, and compare it to that of syndrome-based decoding. Aiming to provide guidelines to practical applications, our complexity analysis differs in several aspects from existing asymptotic complexity analysis, which is typically based on multiplicative fast Fourier transform (FFT) techniques and is usually in big O notation. First, we focus on RS codes over characteristic-2 fields, over which some multiplicative FFT techniques are not applicable. Secondly, due to moderate block lengths of RS codes in practice, our analysis is complete since all terms in the complexities are accounted for. Finally, in addition to fast implementation using additive FFT techniques, we also consider direct implementation, which is still relevant for RS codes with moderate lengths. Comparing the complexities of both syndromeless and syndrome-based decoding algorithms based on direct and fast implementations, we show that syndromeless decoding algorithms have higher complexities than syndrome-based ones for high rate RS codes regardless of the implementation. Both errors-only and errors-and-erasures decoding are considered in this paper. We also derive tighter bounds on the complexities of fast polynomial multiplications based on Cantor's approach and the fast extended Euclidean algorithm.

For an $n_t$ transmit, $n_r$ receive antenna system ($n_t \times n_r$ system), a {\it{full-rate}} space time block code (STBC) transmits $n_{min} = min(n_t,n_r)$ complex symbols per channel use and in general, has an ML-decoding complexity of the order of $M^{n_tn_{min}}$ (considering square designs), where $M$ is the constellation size. In this paper, a scheme to obtain a full-rate STBC for $2^a$ transmit antennas and any $n_r$, with reduced ML-decoding complexity of the order of $M^{n_t(n_{min}-3/4)}$, is presented. The weight matrices of the proposed STBC are obtained from the unitary matrix representations of a Clifford Algebra. For any value of $n_r$, the proposed design offers a reduction from the full ML-decoding complexity by a factor of $M^{3n_t/4}}$. The well known Silver code for 2 transmit antennas is a special case of the proposed scheme. Further, it is shown that the codes constructed using the scheme have higher ergodic capacity than the well known punctured Perfect codes for $n_r < n_t$. Simulation results of the symbol error rates are shown for $8 \times 2$ systems, where the comparison of the proposed code is with the punctured Perfect code for 8 transmit antennas. The proposed code matches the punctured perfect code in error performance, while having reduced ML-decoding complexity and higher ergodic capacity.

In this work, we present hardware and software implementations of flexible polar systematic encoders and decoders. The proposed implementations operate on polar codes of any length less than a maximum and of any rate. We describe the low-complexity, highly parallel, and flexible systematic-encoding algorithm that we use and prove its correctness. Our hardware implementation results show that the overhead of adding code rate and length flexibility is little, and the impact on operation latency minor compared to code-specific versions. Finally, the flexible software encoder and decoder implementations are also shown to be able to maintain high throughput and low latency.

The 3D MIMO code is a robust and efficient space-time coding scheme for the distributed MIMO broadcasting. However, it suffers from the high computational complexity if the optimal maximum-likelihood (ML) decoding is used. In this paper we first investigate the unique properties of the 3D MIMO code and consequently propose a simplified decoding algorithm without sacrificing the ML optimality. Analysis shows that the decoding complexity is reduced from O(M^8) to O(M^{4.5}) in quasi-static channels when M-ary square QAM constellation is used. Moreover, we propose an efficient implementation of the simplified ML decoder which achieves a much lower decoding time delay compared to the classical sphere decoder with Schnorr-Euchner enumeration.

We consider the following problem that arises in outsourced storage: a user stores her data $x$ on a remote server but wants to audit the server at some later point to make sure it actually did store $x$. The goal is to design a (randomized) verification protocol that has the property that if the server passes the verification with some reasonably high probability then the user can rest assured that the server is storing $x$. In this work we present an optimal solution (in terms of the user's storage and communication) while at the same time ensuring that a server that passes the verification protocol with any reasonable probability will store, to within a small \textit{additive} factor, $C(x)$ bits of information, where $C(x)$ is the plain Kolmogorov complexity of $x$. (Since we cannot prevent the server from compressing $x$, $C(x)$ is a natural upper bound.) The proof of security of our protocol combines Kolmogorov complexity with list decoding and unlike previous work that relies upon cryptographic assumptions, we allow the server to have unlimited computational power. To the best of our knowledge, this is the first work that combines Kolmogorov complexity and list decoding. Our framework is general enough to capture extensions where the user splits up $x$ and stores the fragment across multiple servers and our verification protocol can handle non-responsive servers and colluding servers. As a by-product, we also get a proof of retrievability. Finally, our results also have an application in `storage enforcement' schemes, which in turn have an application in trying to update a remote server that is potentially infected with a virus.

Recently, Guo and Xia gave sufficient conditions for an STBC to achieve full diversity when a PIC (Partial Interference Cancellation) or a PIC-SIC (PIC with Successive Interference Cancellation) decoder is used at the receiver. In this paper, we give alternative conditions for an STBC to achieve full diversity with PIC and PIC-SIC decoders, which are equivalent to Guo and Xia's conditions, but are much easier to check. Using these conditions, we construct a new class of full diversity PIC-SIC decodable codes, which contain the Toeplitz codes and a family of codes recently proposed by Zhang, Xu et. al. as proper subclasses. With the help of the new criteria, we also show that a class of PIC-SIC decodable codes recently proposed by Zhang, Shi et. al. can be decoded with much lower complexity than what is reported, without compromising on full diversity.

The common method to estimate an unknown integer parameter vector in a linear model is to solve an integer least squares (ILS) problem. A typical approach to solving an ILS problem is sphere decoding. To make a sphere decoder faster, the well-known LLL reduction is often used as preprocessing. The Babai point produced by the Babai nearest plan algorithm is a suboptimal solution of the ILS problem. First we prove that the success probability of the Babai point as a lower bound on the success probability of the ILS estimator is sharper than the lower bound given by Hassibi and Boyd [1]. Then we show rigorously that applying the LLL reduction algorithm will increase the success probability of the Babai point. Finally we show rigorously that applying the LLL reduction algorithm will also reduce the computational complexity of sphere decoders, which is measured approximately by the number of nodes in the search tree in the literature

Multiple beamforming is realized by singular value decomposition of the channel matrix which is assumed to be known to both the transmitter and the receiver. Bit-Interleaved Coded Multiple Beamforming (BICMB) can achieve full diversity as long as the code rate Rc and the number of employed subchannels S satisfy the condition RcS

Integer least-squares problems, concerned with solving a system of equations where the components of the unknown vector are integer-valued, arise in a wide range of applications. In many scenarios the unknown vector is sparse, i.e., a large fraction of its entries are zero. Examples include applications in wireless communications, digital fingerprinting, and array-comparative genomic hybridization systems. Sphere decoding, commonly used for solving integer least-squares problems, can utilize the knowledge about sparsity of the unknown vector to perform computationally efficient search for the solution. In this paper, we formulate and analyze the sparsity-aware sphere decoding algorithm that imposes $\ell_0$-norm constraint on the admissible solution. Analytical expressions for the expected complexity of the algorithm for alphabets typical of sparse channel estimation and source allocation applications are derived and validated through extensive simulations. The results demonstrate superior performance and speed of sparsity-aware sphere decoder compared to the conventional sparsity-unaware sphere decoding algorithm. Moreover, variance of the complexity of the sparsity-aware sphere decoding algorithm for binary alphabets is derived. The search space of the proposed algorithm can be further reduced by imposing lower bounds on the value of the objective function. The algorithm is modified to allow for such a lower bounding technique and simulations illustrating efficacy of the method are presented. Performance of the algorithm is demonstrated in an application to sparse channel estimation, where it is shown that sparsity-aware sphere decoder performs close to theoretical lower limits.

In this paper we propose two new generic attacks on the Rank Syndrome Decoding (RSD) problem Let $C$ be a random $[n,k]$ rank code over $GF(q^m)$ and let $y=x+e$ be a received word such that $x \in C$ and the $Rank(e)=r$. The first attack is combinatorial and permits to recover an error $e$ of rank weight $r$ in $min(O((n-k)^3m^3q^{r\lfloor\frac{km}{n}\rfloor}, O((n-k)^3m^3q^{(r-1)\lfloor\frac{(k+1)m}{n}\rfloor}))$ operations on $GF(q)$. This attack dramatically improves on previous attack by introducing the length $n$ of the code in the exponent of the complexity, which was not the case in previous generic attacks. which can be considered The second attack is based on a algebraic attacks: based on the theory of $q$-polynomials introduced by Ore we propose a new algebraic setting for the RSD problem that permits to consider equations and unknowns in the extension field $GF(q^m)$ rather than in $GF(q)$ as it is usually the case. We consider two approaches to solve the problem in this new setting. Linearization technics show that if $n \ge (k+1)(r+1)-1$ the RSD problem can be solved in polynomial time, more generally we prove that if $\lceil \frac{(r+1)(k+1)-(n+1)}{r} \rceil \le k$, the problem can be solved with an average complexity $O(r^3k^3q^{r\lceil \frac{(r+1)(k+1)-(n+1)}{r} \rceil})$. We also consider solving with \grob bases for which which we discuss theoretical complexity, we also consider consider hybrid solving with \grob bases on practical parameters. As an example of application we use our new attacks on all proposed recent cryptosystems which reparation the GPT cryptosystem, we break all examples of published proposed parameters, some parameters are broken in less than 1 s in certain cases.

Sampling from the lattice Gaussian distribution is an efficient way for solving the closest vector problem (CVP) in lattice decoding. In this paper, decoding by MCMC-based lattice Gaussian sampling is investigated in full details. First of all, the spectral gap of the transition matrix of the Markov chain induced by the independent Metropolis-Hastings-Klein (MHK) algorithm is derived, dictating an exponential convergence rate to the target lattice Gaussian distribution. Then, the decoding complexity of CVP is derived as $O(e^{d^2(\Lambda, \mathbf{c})/\min_i^2\|\widehat{\mathbf{b}}_i\|})$, where $d(\Lambda, \mathbf{c})$ represents the Euclidean distance between the query point $\mathbf{c}$ and the lattice $\Lambda$, and $\mathbf{\widehat{b}}_i$ is the $i$th Gram-Schmidt vector of the lattice basis $\mathbf{B}$. Furthermore, the decoding radius from the perspective of bounded distance decoding (BDD) given a fixed number of Markov moves $t$ is also derived, revealing a flexible trade-off between the decoding performance and complexity. Finally, by taking advantages of $k$ trial samples from the proposal distribution, the independent multiple-try Metropolis-Klein (MTMK) algorithm is proposed to further enhance the exponential convergence rate. By adjusting $k$, the independent MTMK sampler enjoys a flexible decoding performance, where the independent MHK algorithm is just a case with $k=1$. Additionally, the proposed decoding allows a fully parallel implementation, which is beneficial for the practical interest.

A-posteriori probability (APP) receivers operating over multiple-input, multiple-output channels provide enhanced bit error rate (BER) performance at the cost of increased complexity. However, employing full APP processing over favorable transmission environments, where less efficient approaches may already provide the required performance at a reduced complexity, results in unnecessary processing. For slowly varying channel statistics substantial complexity savings can be achieved by simple adaptive schemes. Such schemes track the BER performance and adjust the complexity of the soft output sphere decoder by adaptively setting the related log-likelihood ratio (LLR) clipping value.

A single input multiple output (SIMO) multiple access channel, with a large number of transmitters sending symbols from a constellation to the receiver of a multi-antenna base station, is considered. The fundamental limits of joint decoding of the signals from all the users using a low complexity convex relaxation of the maximum likelihood decoder (ML, constellation search) is investigated. It has been shown that in a rich scattering environment, and in the asymptotic limit of a large number of transmitters, reliable communication is possible even without employing coding at the transmitters. This holds even when the number of receiver antennas per transmitter is arbitrarily small, with scaling behaviour arbitrarily close to what is achievable with coding. Thus, the diversity of a large system not only makes the scaling law for coded systems similar to that of uncoded systems, but, as we show, also allows efficient decoders to realize close to the optimal performance of maximum-likelihood decoding. However, while there is no performance loss relative to the scaling laws of the optimal decoder, our proposed low-complexity decoder exhibits a loss of the exponential or near-exponential rates of decay of error probability relative to the optimal ML decoder.

Since the classical work of Berlekamp, McEliece and van Tilborg, it is well known that the problem of exact maximum-likelihood (ML) decoding of general linear codes is NP-hard. In this paper, we show that exact ML decoding of a classs of asymptotically good error correcting codes--expander codes, a special case of low density parity check (LDPC) codes--over binary symmetric channels (BSCs) is possible with an expected polynomial complexity. More precisely, for any bit-flipping probability, $p$, in a nontrivial range, there exists a rate region of non-zero support and a family of asymptotically good codes, whose error probability decays exponentially in coding length $n$, for which ML decoding is feasible in expected polynomial time. Furthermore, as $p$ approaches zero, this rate region approaches the channel capacity region. The result is based on the existence of polynomial-time suboptimal decoding algorithms that provide an ML certificate and the ability to compute the probability that the suboptimal decoder yields the ML solution. One such ML certificate decoder is the LP decoder of Feldman; we also propose a more efficient $O(n^2)$ algorithm based on the work of Sipser and Spielman and the Ford-Fulkerson algorithm. The results can be extended to AWGN channels and suggest that it may be feasible to eliminate the error floor phenomenon associated with message-passage decoding of LDPC codes in the high SNR regime. Finally, we observe that the argument of Berlekamp, McEliece and van Tilborg can be used to show that ML decoding of the considered class of codes constructed from LDPC codes with regular left degree, of which the considered expander codes are a special case, remains NP-hard; thus giving an interesting contrast between the worst-case and expected complexities.

For an $n_t$ transmit, $n_r$ receive antenna system ($n_t \times n_r$ system), a {\it{full-rate}} space time block code (STBC) transmits $min(n_t,n_r)$ complex symbols per channel use. In this paper, a scheme to obtain a full-rate STBC for 4 transmit antennas and any $n_r$, with reduced ML-decoding complexity is presented. The weight matrices of the proposed STBC are obtained from the unitary matrix representations of Clifford Algebra. By puncturing the symbols of the STBC, full rate designs can be obtained for $n_r < 4$. For any value of $n_r$, the proposed design offers the least ML-decoding complexity among known codes. The proposed design is comparable in error performance to the well known perfect code for 4 transmit antennas while offering lower ML-decoding complexity. Further, when $n_r < 4$, the proposed design has higher ergodic capacity than the punctured Perfect code. Simulation results which corroborate these claims are presented.

This paper considers the average complexity of maximum likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path taken through a Markov graph. Integrated with the Viterbi algorithm (VA), complexity reduction methods such as the sphere decoder often use the sum log likelihood (SLL) of a Markov path as a bound to disprove the optimality of other Markov path sets and to consequently avoid exhaustive path search. In this paper, it is shown that SLL-based optimality tests are inefficient if one fixes the coding memory and takes the codeword length to infinity. Alternatively, optimality of a source symbol at a given time index can be testified using bounds derived from log likelihoods of the neighboring symbols. It is demonstrated that such neighboring log likelihood (NLL)-based optimality tests, whose efficiency does not depend on the codeword length, can bring significant complexity reduction to ML decoding of convolutional codes. The results are generalized to ML sequence detection in a class of discrete-time hidden Markov systems.

Non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) having large dimensions are attractive because they can simultaneously achieve both high spectral efficiencies (same spectral efficiency as in V-BLAST for a given number of transmit antennas) {\em as well as} full transmit diversity. Decoding of non-orthogonal STBCs with hundreds of dimensions has been a challenge. In this paper, we present a probabilistic data association (PDA) based algorithm for decoding non-orthogonal STBCs with large dimensions. Our simulation results show that the proposed PDA-based algorithm achieves near SISO AWGN uncoded BER as well as near-capacity coded BER (within about 5 dB of the theoretical capacity) for large non-orthogonal STBCs from CDA. We study the effect of spatial correlation on the BER, and show that the performance loss due to spatial correlation can be alleviated by providing more receive spatial dimensions. We report good BER performance when a training-based iterative decoding/channel estimation is used (instead of assuming perfect channel knowledge) in channels with large coherence times. A comparison of the performances of the PDA algorithm and the likelihood ascent search (LAS) algorithm (reported in our recent work) is also presented.

Linear Programming (LP) decoding of Low-Density Parity-Check (LDPC) codes has attracted much attention in the research community in the past few years. LP decoding has been derived for binary and nonbinary linear codes. However, the most important problem with LP decoding for both binary and nonbinary linear codes is that the complexity of standard LP solvers such as the simplex algorithm remains prohibitively large for codes of moderate to large block length. To address this problem, two low-complexity LP (LCLP) decoding algorithms for binary linear codes have been proposed by Vontobel and Koetter, henceforth called the basic LCLP decoding algorithm and the subgradient LCLP decoding algorithm. In this paper, we generalize these LCLP decoding algorithms to nonbinary linear codes. The computational complexity per iteration of the proposed nonbinary LCLP decoding algorithms scales linearly with the block length of the code. A modified BCJR algorithm for efficient check-node calculations in the nonbinary basic LCLP decoding algorithm is also proposed, which has complexity linear in the check node degree. Several simulation results are presented for nonbinary LDPC codes defined over Z_4, GF(4), and GF(8) using quaternary phase-shift keying and 8-phase-shift keying, respectively, over the AWGN channel. It is shown that for some group-structured LDPC codes, the error-correcting performance of the nonbinary LCLP decoding algorithms is similar to or better than that of the min-sum decoding algorithm.

In this paper, we present a low-complexity joint detection-decoding algorithm for nonbinary LDPC codedmodulation systems. The algorithm combines hard-decision decoding using the message-passing strategy with the signal detector in an iterative manner. It requires low computational complexity, offers good system performance and has a fast rate of decoding convergence. Compared to the q-ary sum-product algorithm (QSPA), it provides an attractive candidate for practical applications of q-ary LDPC codes.

Linear Programming (LP) decoding of Low-Density Parity-Check (LDPC) codes has attracted much attention in the research community in the past few years. The aim of LP decoding is to develop an algorithm which has error-correcting performance similar to that of the Sum-Product (SP) decoding algorithm, while at the same time it should be amenable to mathematical analysis. The LP decoding algorithm has also been extended to nonbinary linear codes by Flanagan et al. However, the most important problem with LP decoding for both binary and nonbinary linear codes is that the complexity of standard LP solvers such as the simplex algorithm remain prohibitively large for codes of moderate to large block length. To address this problem, Vontobel et al. proposed a low complexity LP decoding algorithm for binary linear codes which has complexity linear in the block length. In this paper, we extend the latter work and propose a low-complexity LP decoding algorithm for nonbinary linear codes. We use the LP formulation proposed by Flanagan et al. as a basis and derive a pair of primal-dual LP formulations. The dual LP is then used to develop the low-complexity LP decoding algorithm for nonbinary linear codes. In contrast to the binary low-complexity LP decoding algorithm, our proposed algorithm is not directly related to the nonbinary SP algorithm. Nevertheless, the complexity of the proposed algorithm is linear in the block length and is limited mainly by the maximum check node degree. As a proof of concept, we also present a simulation result for a $[80,48]$ LDPC code defined over $\mathbb{Z}_4$ using quaternary phase-shift keying over the AWGN channel, and we show that the error-correcting performance of the proposed LP decoding algorithm is similar to that of the standard LP decoding using the simplex solver.

While the capacity, feasibility and methods to obtain codes for network coding problems are well studied, the decoding procedure and complexity have not garnered much attention. In this work, we pose the decoding problem at a sink node in a network as a marginalize a product function (MPF) problem over a Boolean semiring and use the sum-product (SP) algorithm on a suitably constructed factor graph to perform iterative decoding. We use \textit{traceback} to reduce the number of operations required for SP decoding at sink node with general demands and obtain the number of operations required for decoding using SP algorithm with and without traceback. For sinks demanding all messages, we define \textit{fast decodability} of a network code and identify a sufficient condition for the same. Next, we consider the in-network function computation problem wherein the sink nodes do not demand the source messages, but are only interested in computing a function of the messages. We present an MPF formulation for function computation at the sink nodes in this setting and use the SP algorithm to obtain the value of the demanded function. The proposed method can be used for both linear and nonlinear as well as scalar and vector codes for both decoding of messages in a network coding problem and computing linear and nonlinear functions in an in-network function computation problem.

Motivated by the recent developments in the Space Shift Keying (SSK) and Spatial Modulation (SM) systems which employ Time-Orthogonal Pulse Shaping (TOPS) filters to achieve transmit diversity gains, we propose TOPS for Space-Time Block Codes (STBC). We show that any STBC whose set of weight matrices partitions into P subsets under the equivalence relation termed as Common Support Relation can be made P -group decodable by properly employing TOPS waveforms across space and time. Furthermore, by considering some of the well known STBCs in the literature we show that the order of their Maximum Likelihood decoding complexity can be greatly reduced by the application of TOPS.

The design of linear STBCs offering a low-complexity ML decoding using the well known Sphere Decoder (SD) has been extensively studied in last years. The first considered approach to derive design criteria for the construction of such codes is based on the Hurwitz-Radon (HR) Theory for mutual orthogonality between the weight matrices defining the linear code. This appproach served to construct new families of codes admitting fast sphere decoding such as multi-group decodable, fast decodable, and fast-group decodable codes. In a second Quadratic Form approach, the Fast Sphere Decoding (FSD) complexity of linear STBCs is captured by a Hurwitz Radon Quadratic Form (HRQF) matrix based in its essence on the HR Theory. In this work, we revisit the structure of weight matrices for STBCs to admit Fast Sphere decoding. We first propose novel sufficient conditions and design criteria for reduced-complexity ML decodable linear STBCs considering an arbitrary number of antennas and linear STBCs of an arbitrary coding rate. Then we apply the derived criteria to the three families of codes mentioned above and provide analytical proofs showing that the FSD complexity depends only on the weight matrices and their ordering and not on the channel gains or the number of antennas and explain why the so far used HR theory-based approaches are suboptimal.

Algebraic space-time coding allows for reliable data exchange across fading multiple-input multiple-output channels. A powerful technique for decoding space-time codes in Maximum-Likelihood (ML) decoding, but well-performing and widely-used codes such as the Golden code often suffer from high ML-decoding complexity. In this article, a recursive algorithm for decoding general algebraic space-time codes of arbitrary dimension is proposed, which reduces the worst-case decoding complexity from $O(|S|^{n^2})$ to $O(|S|^n)$.

Full-rate space time codes (STC) with rate = number of transmit antennas have high multiplexing gain, but high decoding complexity even when decoded using reduced-complexity decoders such as sphere or QRDM decoders. In this paper, we introduce a new code property of STC called block-orthogonal property, which can be exploited by QR-decomposition-based decoders to achieve significant decoding complexity reduction without performance loss. We show that such complexity reduction principle can benefit the existing algebraic codes such as Perfect and DjABBA codes due to their inherent (but previously undiscovered) block-orthogonal property. In addition, we construct and optimize new full-rate BOSTC (Block-Orthogonal STC) that further maximize the QRDM complexity reduction potential. Simulation results of bit error rate (BER) performance against decoding complexity show that the new BOSTC outperforms all previously known codes as long as the QRDM decoder operates in reduced-complexity mode, and the code exhibits a desirable complexity saturation property.

A novel distributed space-time block code (STBC) for the next generation mobile TV broadcasting is proposed. The new code provides efficient performance within a wide range of power imbalance showing strong adaptivity to the single frequency network (SFN) broadcasting deployments. The new code outperforms existing STBCs with equivalent decoding complexity and approaches those with much higher complexities.

An irregular LDGM-LDPC code is studied as a sub-code of an LDPC code with some randomly \emph{punctured} output-bits. It is shown that the LDGM-LDPC codes achieve rates arbitrarily close to the channel-capacity of the binary-input symmetric-output memoryless (BISOM) channel with bounded \emph{complexity}. The measure of complexity is the average-degree (per information-bit) of the check-nodes for the factor-graph of the code. A lower-bound on the average degree of the check-nodes of the irregular LDGM-LDPC codes is obtained. The bound does not depend on the decoder used at the receiver. The stability condition for decoding the irregular LDGM-LDPC codes over the binary-erasure channel (BEC) under iterative-decoding with message-passing is described.

The standard algebraic decoding algorithm of cyclic codes $[n,k,d]$ up to the BCH bound $t$ is very efficient and practical for relatively small $n$ while it becomes unpractical for large $n$ as its computational complexity is $O(nt)$. Aim of this paper is to show how to make this algebraic decoding computationally more efficient: in the case of binary codes, for example, the complexity of the syndrome computation drops from $O(nt)$ to $O(t\sqrt n)$, and that of the error location from $O(nt)$ to at most $\max \{O(t\sqrt n), O(t^2\log(t)\log(n))\}$.

In the landmark paper by Hassibi and Hochwald, it is claimed without proof that the upper triangular matrix R encountered during the sphere decoding of any linear dispersion code is full-ranked whenever the rate of the code is less than the minimum of the number of transmit and receive antennas. In this paper, we show that this claim is true only when the number of receive antennas is at least as much as the number of transmit antennas. We also show that all known families of high rate (rate greater than 1 complex symbol per channel use) multigroup ML decodable codes have rank-deficient R matrix even when the criterion on rate is satisfied, and that this rank-deficiency problem arises only in asymmetric MIMO with number of receive antennas less than the number of transmit antennas. Unlike the codes with full-rank R matrix, the average sphere decoding complexity of the STBCs whose R matrix is rank-deficient is polynomial in the constellation size. We derive the sphere decoding complexity of most of the known high rate multigroup ML decodable codes, and show that for each code, the complexity is a decreasing function of the number of receive antennas.

This paper (Part of the content of this manuscript has been accepted for presentation in IEEE Globecom 2008, to be held in New Orleans) deals with low maximum likelihood (ML) decoding complexity, full-rate and full-diversity space-time block codes (STBCs), which also offer large coding gain, for the 2 transmit antenna, 2 receive antenna ($2\times 2$) and the 4 transmit antenna, 2 receive antenna ($4\times 2$) MIMO systems. Presently, the best known STBC for the $2\times2$ system is the Golden code and that for the $4\times2$ system is the DjABBA code. Following the approach by Biglieri, Hong and Viterbo, a new STBC is presented in this paper for the $2\times 2$ system. This code matches the Golden code in performance and ML-decoding complexity for square QAM constellations while it has lower ML-decoding complexity with the same performance for non-rectangular QAM constellations. This code is also shown to be \emph{information-lossless} and \emph{diversity-multiplexing gain} (DMG) tradeoff optimal. This design procedure is then extended to the $4\times 2$ system and a code, which outperforms the DjABBA code for QAM constellations with lower ML-decoding complexity, is presented. So far, the Golden code has been reported to have an ML-decoding complexity of the order of $M^4$ for square QAM of size $M$. In this paper, a scheme that reduces its ML-decoding complexity to $M^2\sqrt{M}$ is presented.

In this paper, Space-Time Block Codes (STBCs) with reduced Sphere Decoding Complexity (SDC) are constructed for two-user Multiple-Input Multiple-Output (MIMO) fading multiple access channels. In this set-up, both the users employ identical STBCs and the destination performs sphere decoding for the symbols of the two users. First, we identify the positions of the zeros in the $\textbf{R}$ matrix arising out of the Q-R decomposition of the lattice generator such that (i) the worst case SDC (WSDC) and (ii) the average SDC (ASDC) are reduced. Then, a set of necessary and sufficient conditions on the lattice generator is provided such that the $\textbf{R}$ matrix has zeros at the identified positions. Subsequently, explicit constructions of STBCs which results in the reduced ASDC are presented. The rate (in complex symbols per channel use) of the proposed designs is at most $2/N_{t}$ where $N_{t}$ denotes the number of transmit antennas for each user. We also show that the class of STBCs from complex orthogonal designs (other than the Alamouti design) reduce the WSDC but not the ASDC.

The differential encoding/decoding setup introduced by Kiran et al, Oggier-Hassibi and Jing-Jafarkhani for wireless relay networks that use codebooks consisting of unitary matrices is extended to allow codebooks consisting of scaled unitary matrices. For such codebooks to be usable in the Jing-Hassibi protocol for cooperative diversity, the conditions involving the relay matrices and the codebook that need to be satisfied are identified. Using the algebraic framework of extended Clifford algebras, a new class of Distributed Differential Space-Time Codes satisfying these conditions for power of two number of relays and also achieving full cooperative diversity with a low complexity sub-optimal receiver is proposed. Simulation results indicate that the proposed codes outperform both the cyclic codes as well as the circulant codes. Furthermore, these codes can also be applied as Differential Space-Time codes for non-coherent communication in classical point to point multiple antenna systems.