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This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. This paper proposes to tackle this problem by using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML), First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA meta-learning (CAVIA). Both offline and online solutions are developed. In the latter case, an integrated online meta-learning and adaptive pilot number selection scheme is proposed. Numerical results validate the advantages of meta-learning as compared to training schemes that either do not leverage prior transmissions or apply a standard joint learning algorithms on previously received data. [ABSTRACT FROM AUTHOR]

Polar codes have gained extensive attention during the past few years and recently they have been selected for the next generation of wireless communications standards (5G). Successive-cancellation-based (SC-based) decoders, such as SC list (SCL) and SC flip (SCF), provide a reasonable error performance for polar codes at the cost of low decoding speed. Fast SC-based decoders, such as Fast-SSC, Fast-SSCL, and Fast-SSCF, identify the special constituent codes in a polar code graph off-line, produce a list of operations, store the list in memory, and feed the list to the decoder to decode the constituent codes in order efficiently, thus increasing the decoding speed. However, the list of operations is dependent on the code rate and as the rate changes, a new list is produced, making fast SC-based decoders not rate-flexible. In this paper, we propose a completely rate-flexible fast SC-based decoder by creating the list of operations directly in hardware, with low implementation complexity. We further propose a hardware architecture implementing the proposed method and show that the area occupation of the rate-flexible fast SC-based decoder in this paper is only $\text{38}{\%}$ of the total area of the memory-based base-line decoder when 5G code rates are supported. [ABSTRACT FROM AUTHOR]

Deep structures consisting of many layers of nonlinearities have a high potential of expressing complex relations if properly initialized. Autoencoders play a complementary role in training a deep structure by initializing each layer in a greedy unsupervised manner. Due to the high capacity presented by autoencoders, these structures need to be regularized. While mathematical regularizers (based on weight decay, sparsity, etc.) and structural ones (by way of, e.g., denoising and dropout) have been well studied in the literature, quite a few papers have addressed the problem of training autoencoder with non-smooth regularization. In this paper, we address the problem of training autoencoder with non-smooth regularization. We propose an efficient algorithm and mathematically prove that it is convergent, where the regularizer needs to be proximable. As one of major applications of the proposed method, we get focused on the problem of sparse autoencoders and show that the new training method leads to better disentangling of factors of variation. [ABSTRACT FROM AUTHOR]

This paper considers the joint fronthaul compression and transmit beamforming design for the uplink cloud radio access network (C-RAN), in which multi-antenna users communicate with a cloud-computing based centralized processor (CP) through multi-antenna base-stations (BSs) serving as relay nodes. A compress-and-forward relaying strategy, named the virtual multiple-access channel (VMAC) scheme, is employed, in which the BSs can either perform single-user compression or Wyner-Ziv coding to quantize the received signals and send the quantization bits to the CP via capacity-limited fronthaul links; the CP performs successive decoding with either successive interference cancellation (SIC) receiver or linear minimum-mean square-error (MMSE) receiver. Under this setup, this paper investigates the joint optimization of the transmit beamformers and the quantization noise covariance matrices for maximizing the network utility. A novel weighted minimum-mean-square-error successive convex approximation (WMMSE-SCA) algorithm is first proposed for maximizing the weighted sum rate under the user transmit power and fronthaul capacity constraints with single-user compression. Assuming a heuristic decompression order, the proposed algorithm is then adapted for optimizing the transmit beamforming and fronthaul compression under Wyner-Ziv coding. This paper also proposes a low-complexity separate design consisting of optimizing transmit beamformers for the Gaussian vector multiple-access channel along with per-antenna quantizers with uniform quantization noise levels across the antennas at each BS. Numerical results show that majority of the performance gain brought by C-RAN comes from the implementation of SIC at the CP. Furthermore, the low complexity separate design already performs very close to the optimized joint design in regime of practical interest. [ABSTRACT FROM PUBLISHER]

This paper studies and analyzes a multi-pair MIMO two-way relay (TWR) system from a principal-angle perspective. In this system, users are grouped into pairs and two users in each pair exchange information through a common relay. The transmission scheme consists of two phases. In the uplink phase, the relay’s receive signal space is divided into two subspaces for each pair: in one subspace, the signals of the two users are near-parallel, and physical-layer network coding (PNC) decoding is applied to retrieve network-coded messages; in the other subspace, the two user signals are near-orthogonal, and complete decoding (CD) is applied to retrieve individual user messages. In the downlink phase, the relay’s transmit signal space is split into orthogonal subspaces for each pair, and in each subspace a common message is broadcasted to the two users. Our main contribution is to establish a critical link between the transmission scheme and the concept of principal angle in linear algebra. In particular, principal angle quantizes the degree of orthogonality between the user signals and helps determining how to partition the relay’s receive signal space into PNC decoding subspace and CD decoding subspace. Building on that, we discuss the optimal design of user/relay precoders for both uplink and downlink phases to maximize the asymptotic sum-rate of the proposed scheme at high signal-to-noise ratio (SNR). The analysis and numerical results indicate that our scheme performs close to the cut-set outer bound and significantly outperforms the existing schemes in the literature. [ABSTRACT FROM AUTHOR]

Joint source-channel coding schemes based on analog mappings for point-to-point channels have recently gained attention for their simplicity and low delay. In this paper, these schemes are extended either to scenarios with or without side information at the decoders to transmit multiple descriptions of a Gaussian source over independent parallel channels. They are based on a lattice scaling approach together with bandwidth reduction analog mappings adapted for this multiple description scenario. The rationale behind lattice scaling is to improve performance through bandwidth expansion. Another important contribution of this paper is the proof of the separation theorem for the communication scenario of multiple description with side information at the decoders. This proof allows us to compare the performance of the proposed schemes with the theoretical limits when considering AWGN channels. Finally, when the channels are parallel slow-fading Rayleigh, the proposed schemes are optimal in terms of distortion exponent. [ABSTRACT FROM PUBLISHER]

Polar codes are the first error-correcting codes to provably achieve channel capacity, asymptotically in code length, with an explicit construction. However, under successive-cancellation decoding, polar codes require very long code lengths to compete with existing modern codes. Nonetheless, the successive cancellation algorithm enables very-low-complexity implementations in hardware, due to the regular structure exhibited by polar codes. In this paper, we present an improved architecture for successive-cancellation decoding of polar codes, making use of a novel semi-parallel, encoder-based partial-sum computation module. We also provide quantization results for realistic code length N=2^15, and explore various optimization techniques such as a chained processing element and a variable quantization scheme. This design is shown to scale to code lengths of up to N=2^21, enabled by its low logic use, low register use and simple datapaths, limited almost exclusively by the amount of available SRAM. It also supports an overlapped loading of frames, allowing full-throughput decoding with a single set of input buffers. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of beam alignment for millimeter wave (mmWave) communications, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). By exploiting the sparse scattering nature of mmWave channels, the beam alignment problem is formulated as a sparse encoding and phaseless decoding problem. More specifically, the problem of interest involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of a sparse signal from compressive phaseless measurements. A sparse bipartite graph code algorithm is developed for sparse encoding and phaseless decoding. Our theoretical analysis shows that, in the noiseless case, our proposed algorithm can perfectly recover the support and magnitude of the sparse signal with a probability exceeding a pre-specified value from $\mathcal {O}(K^2)$ measurements, where $K$ is the number of nonzero entries of the sparse signal. The proposed algorithm has a simple decoding procedure which is computationally efficient and noise robust. Simulation results show that our proposed method renders reliable beam alignment even in the low signal-to-noise ratio regime and presents a clear performance advantage over existing methods. [ABSTRACT FROM AUTHOR]

Polar codes have been adopted in the 5th generation communication standard (5G). Successive-cancellation list (SCL) decoding, a promising solution to decode polar codes, provides good tradeoff between error-correction performance and implementation complexity. To meet high flexibility and scalability requirements of Cloud or Open random access networks (RANs) for the 5G or future communication standards, this paper presents software list decoding architectures on graphics processing units (GPUs) and considers special nodes to reduce the decoding complexity. In order to improve the throughput and latency performance, a GPU-based adaptive mapping strategy taking advantage of the GPU architecture is proposed. Without considering the data transfer procedure between host and device, the proposed adaptive list decoder on NVIDIA RTX3090 for the code (1024, 512) with list sizes $L=8$ and $L=32$ can achieve above 30 Gbps coded throughput at the frame error rates of $10^{-6}$ and $10^{-7}$ , respectively. Compared to the state-of-the-art software works, the proposed decoder achieves about $\times 1.78$ to $\times 9.07$ throughput speedups. [ABSTRACT FROM AUTHOR]

Error-correction codes (ECC) and redundant array of independent disks (RAID) are commonly used to protect NAND-based solid state drives (SSD) from serious noise/disturb. In order to tolerate more errors, common solutions are more complex ECCs and RAIDs. Meanwhile, due to the error-correction mechanisms and flash translation layer, NAND-based SSDs inevitably generate abundant redundant information. The theme of this paper is to show that instead of designing new error correction mechanisms from scratch, the NAND-based SSD reliability can be greatly improved by utilizing current decoders to explore the inherent information between redundant pages. We first propose a joint decoder to recover multiple pages by utilizing redundant pages within a stripe of ECC/RAID, and then we consider a joint decoder to recover multiple pages by utilizing write-amplified (WA) pages. The simulation results show that with the proposed algorithm, a performance gain of as much as 1.8 dB can be obtained. The theoretical study reveals that the joint decoding model can be studied from the perspective of relay channel model, and this helps to explore how much information can be reliably stored with the RAID/WA system in SSD. [ABSTRACT FROM AUTHOR]

Polar codes have received recent attention due to their potential to be applied in advanced wireless communication protocols such as the fifth generation mobile communication system (5G). Among the existing decoding algorithms, Belief Propagation (BP) exhibits high-throughput, low-latency, and soft output with a high hardware cost. Stochastic computing, as a form of approximate computing, provides a potential low-cost implementation solution for the BP algorithm. However, existing stochastic BP decoders suffer from a relatively long decoding latency resulting in low hardware efficiency. In this paper, a novel bit-wise iterative stochastic decoding architecture for the BP algorithm is proposed to improve the throughput and hardware efficiency. By utilizing the frozen bits of polar codes and stochastic computing, multiple novel optimization methods are presented to further speed up convergence and increase the hardware efficiency. [ABSTRACT FROM AUTHOR]

The practice of compressed sensing suffers importantly in terms of the efficiency/accuracy trade-off when acquiring noisy signals prior to measurement. It is rather common to find results treating the noise affecting the measurements, avoiding in this way to face the so-called noise-folding phenomenon, related to the noise in the signal, eventually amplified by the measurement procedure. In this paper, we present two new decoding procedures, combining \ell_1-minimization followed by either a regularized selective least p-powers or an iterative hard thresholding, which not only are able to reduce this component of the original noise, but also have enhanced properties in terms of support identification with respect to the sole \ell_1-minimization or iteratively re-weighted \ell_1-minimization. We prove such features, providing relatively simple and precise theoretical guarantees. We additionally confirm and support the theoretical results by extensive numerical simulations, which give a statistics of the robustness of the new decoding procedures with respect to more classical methods based on \ell_1-minimization. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new algorithm for compressive sensing that makes use of binary measurement matrices and achieves exact recovery of ultrasparse vectors in a single pass and without any iterations. Due to its noniterative nature, our algorithm is hundreds of times faster than $\ell _1$ -norm minimization and methods based on expander graphs, both of which require multiple iterations. Our algorithm can accommodate nearly sparse vectors, in which case, it recovers an index set of the largest components, and can also accommodate burst noise measurements. Compared to compressive sensing methods that are guaranteed to achieve exact recovery of all sparse vectors, our method requires fewer measurements. However, methods that achieve statistical recovery, that is, recovery of almost all but not all sparse vectors, can require fewer measurements than our method. [ABSTRACT FROM AUTHOR]

The maximum-likelihood (ML) decoder for symbol detection in large multiple-input multiple-output wireless communication systems is typically computationally prohibitive. In this paper, we study a popular and practical alternative, namely the box-relaxation optimization (BRO) decoder, which is a natural convex relaxation of the ML. For independent identically distributed real Gaussian channels with additive Gaussian noise, we obtain exact asymptotic expressions for the symbol error rate (SER) of the BRO. The formulas are particularly simple, they yield useful insights, and they allow accurate comparisons to the matched-filter bound (MFB) and to linear decoders, such as zero-forcing and linear minimum mean square error. For binary phase-shift keying signals, the SER performance of the BRO is within 3 dB of the MFB for square systems, and it approaches the MFB as the number of receive antennas grows large compared to the number of transmit antennas. Our analysis further characterizes the empirical density function of the solution of the BRO, and shows that error events for any fixed number of symbols are asymptotically independent. The fundamental tool behind the analysis is the convex Gaussian min–max theorem. [ABSTRACT FROM AUTHOR]

This paper presents an efficient combined bit-flipping (BF) and stochastic low-density parity-check decoder, where a BF decoder is used to achieve a reduction in decoding cycles. A node-wise probability tracer is adopted at each variable node (VN) in order to achieve a BER performance comparable to the normalized min-sum algorithm, where check-to-variable (C2V) messages are used as inputs, rather than the variable-to-check (V2C) messages adopted in previous stochastic decoders. The complexity of the VN units is greatly reduced by sharing common units used in the generation of V2C messages together with a probability tracer. The C2V-based probability tracer enables the design of a decoder that provides a short critical path. The proposed methods are demonstrated by designing a (2048, 1723) decoder that is implemented in a 90 nm process. A total of 1460 K logic gates are integrated in a decoder that has an area of 4.12 \mboxmm^2 and achieves a coded throughput of 39.3 Gb/s at a clock frequency of 749 MHz. To the best of the authors’ knowledge, the proposed decoder achieves the best normalized throughput-to-area ratio among the stochastic decoders reported in the open literatures. [ABSTRACT FROM AUTHOR]

In this paper, we consider the transceiver and relay design for the multiple-input multiple-output (MIMO) cellular two-way relay channel (cTWRC), where a multi-antenna base station (BS) exchanges information with multiple multiantenna mobile stations via a multi-antenna relay station (RS). We propose a novel two-way relaying scheme to approach the sum capacity of the MIMO cTWRC. A key contribution of this work is a new nonlinear lattice-based precoding technique to precompensate the interstream interference, so as to achieve efficient interference-free lattice decoding at the relay. We derive sufficient conditions for the proposed scheme to asymptotically achieve the sum capacity of the MIMO cTWRC in the high signal-to-noise ratio (SNR) regime. To fully exploit the potential of the proposed scheme, we also investigate the optimal power allocation at the BS and the RS to maximize the weighted sum-rate of the MIMO cTWRC in the general SNR regime. It is shown that the problem can be formulated as a monotonic program, and a polyblock outer approximation algorithm is developed to find the globally optimal solution with guaranteed convergence. We demonstrate by numerical results that the proposed scheme significantly outperforms the existing schemes and closely approaches the sum capacity of the MIMO cTWRC in the high SNR regime. [ABSTRACT FROM PUBLISHER]

In this paper, the paradigm of expectation propagation (EP) algorithm in large-scale MIMO detection is extended by the sampling decoding in an Markov chain Monte Carlo way to boost the approximation of the target posterior distribution. The proposed EP-based sampling decoding scheme not only theoretically addresses the inherent convergence problem of EP, but also is able to achieve the near-optimal decoding performance with the increment of Markov moves. Specifically, the EP-based independent Metropolis-Hastings (MH) is proposed to guarantee the exponential convergence to the target posterior distribution, thus bridging the EP detector and the sampling decoding as a whole. Meanwhile, the output yielded by the EP detector also provides a good initial setup for the sampling decoding, which results in a better convergence performance in the approximation. To further improve the convergence performance and the decoding efficiency, the EP-based Gibbs sampling is given, where the choice of the standard deviation of the discrete Gaussian distribution in the Markov mixing is also studied for a better decoding performance. Moreover, we extend the proposed EP-based Gibbs sampling decoding to the soft-output decoding in MIMO bit-interleaved coded modulation (BICM) systems, which enjoys a flexible decoding trade-off between performance and complexity by the number of Markov moves. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to design interference alignment (IA) precoding and decoding matrices for arbitrary MIMO X networks. The proposed algorithm is rooted in the homotopy continuation techniques commonly used to solve systems of nonlinear equations. Homotopy methods find the solution of a target system by smoothly deforming the solution of a start system which can be trivially solved. Unlike previously proposed IA algorithms, the homotopy continuation technique allows us to solve the IA problem for both unstructured (i.e., generic) and structured channels such as those that arise when time or frequency symbol extensions are jointly employed with the spatial dimension. To this end, we consider an extended system of bilinear equations that include the standard alignment equations to cancel the interference, and a new set of bilinear equations that preserve the desired dimensionality of the signal spaces at the intended receivers. We propose a simple method to obtain the start system by randomly choosing a set of precoders and decoders, and then finding a set of channels satisfying the system equations, which is a linear problem. Once the start system is available, standard prediction and correction techniques are applied to track the solution all the way to the target system. We analyze the convergence of the proposed algorithm and prove that, for many feasible systems and a sufficiently small continuation parameter, the algorithm converges with probability one to a perfect IA solution. The simulation results show that the proposed algorithm is able to consistently find solutions achieving the maximum number of degrees of freedom in a variety of MIMO X networks with or without symbol extensions. Further, the algorithm provides insights into the feasibility of IA in MIMO X networks for which theoretical results are scarce. [ABSTRACT FROM PUBLISHER]

This paper proposes for single carrier signaling a simple structure that combines turbo equalization and decoding of correlated sources in multipath-rich multiuser Rayleigh fading multiple access channels (MAC), where the correlation between the sources is exploited by vertical iterations (VI) between the decoders. The bit-flipping model with a flipping probability pe is used to express the correlation ρ between the sources as ρ = 1-2pe. The proposed simple structure, spatially concatenated codes (SpCC), can achieve turbo-like performance over MAC channels suffering from severe inter-symbol interference (ISI), even though it uses short memory convolutional codes. First of all, to achieve turbo-like performance we introduce VIs to exploit the knowledge about the source correlation by exchanging extrinsic log-likelihood ratio (LLR) between the two decoders. We then add a rate-1 doped accumulator (D-ACC) to flexibly adapt for the variation of correlations between the sources. The results of computer simulations and extrinsic information transfer (EXIT) analysis confirm that in multipath-rich environments the proposed structure can achieve excellent performances, 1.02-1.28 dB away from the Slepian-Wolf-Shannon limit, at 1% outage probability for 0 <; ρ ≤ 1. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a robust lattice alignment design for K-user quasi-static multiple-input multiple-output (MIMO) interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite signal-to-noise ratio (SNR) or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method compared with the time-division multiple-access (TDMA), two-stage maximum-likelihood (ML) decoding, generalized Han–Kobayashi (HK), distributive IA and conventional IA methods in the literature. [ABSTRACT FROM PUBLISHER]

Group testing is the problem of identifying $K$ defective items among $n$ items by pooling groups of items. In this paper, we design group testing algorithms for approximate recovery with order-optimal sample complexity by leveraging design and analysis tools from modern sparse-graph coding theory. Our algorithm, SAFFRON, recovers at least $(1-\varepsilon)K$ defective items w.p. $1-K/n^r$ with $m=2(1+r)C(\varepsilon)K\log _2{n}$ tests, where $\varepsilon$ is an arbitrarily small constant, $C(\varepsilon)$ is a precisely characterizable constant, and $r$ is any positive integer. The decoding complexity is $\Theta (K\log n)$. We also propose variations of SAFFRON, which are robust to noise and unknown offsets. For example, for $n \simeq 4.3\times 10^9$ and $K = 128$ , our algorithm is observed to recover all defective items with $m \simeq 8.3\times 10^{5}$ tests, even in the presence of noisy test results. Moreover, the decoding time takes less than 4 seconds on a laptop with a 2 GHz Intel Core i7 and 8 GB memory. [ABSTRACT FROM AUTHOR]

Interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance the capacity of a wireless communication network. This paper proposes a grouping-based interference alignment (GIA) with optimized IA-Cell assignment for the multiple cells interfering multiple-input multiple-output (MIMO) multiple access channel (MAC) network under limited feedback. This work consists of three main parts: 1) an improved version (including some new improvements) of the GIA with respect to the degrees of freedom (DoF) and optimal linear transceiver design is provided, which allows for low-complexity and distributed implementation; 2) based on the GIA, the concept of IA-Cell assignment is introduced. Three IA-Cell assignment algorithms are proposed with different backhaul overhead and their DoF and rate performance is investigated; 3)the performance of the proposed GIA algorithms is studied under limited feedback of IA precoders. To enable efficient feedback, a dynamic feedback bit allocation (DBA) problem is formulated and solved in closed-form. The practical implementation, the backhaul overhead requirements, and the complexity of the proposed algorithms are analyzed. Numerical results show that our proposed algorithms greatly outperform the traditional GIA under both unlimited and limited feedback. [ABSTRACT FROM PUBLISHER]

Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding, and cryptography. In this paper, lattice reduction technique is adopted to Gibbs sampler for lattice Gaussian sampling. First, with respect to lattice Gaussian distribution, we show the convergence rate of systematic scan Gibbs sampling is characterized by the Hirschfeld-Gebelein-Rényi maximal correlation among the multivariate of being sampled. Then, Lattice-reduction-aided Gibbs algorithm is proposed to sample from an equivalent lattice Gaussian distribution but with less correlated multivariate, thus leading to a better Markov mixing. After that, we extend the proposed lattice-reduction-aided Gibbs sampling to lattice decoding, where the choice of the standard deviation for the sampling is fully investigated. A customized solution that suits for each specific decoding case by Euclidean distance is given, which results in a better tradeoff between Markov mixing and sampler decoding. Based on it, a startup mechanism is also proposed for Gibbs sampler decoding, where decoding complexity can be reduced without performance loss. Moreover, the recycling Gibbs sampling that exploits the potential of samples is also considered to improve the decoding performance in lattice decoding. Simulation results based on large-scale uncoded multiple-input multiple-output detection are presented to confirm the performance gain and complexity reduction. [ABSTRACT FROM AUTHOR]

We propose a fast and near-optimal approach to joint channel-estimation, equalization, and decoding of coded single-carrier (SC) transmissions over frequency-selective channels with few-bit analog-to-digital converters (ADCs). Our approach leverages parametric bilinear generalized approximate message passing to reduce the implementation complexity of joint channel estimation and (soft) symbol decoding to that of a few fast Fourier transforms. Furthermore, it learns and exploits sparsity in the channel impulse response. This paper is motivated by millimeter-wave systems with bandwidths on the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast processing all lead to significant reductions in power consumption and implementation cost. We numerically demonstrate our approach using signals and channels generated according to the IEEE 802.11ad wireless local area network standard, in the case that the receiver uses analog beamforming and a single ADC. [ABSTRACT FROM AUTHOR]

In this paper, we derive mutual information based upper bounds on the number of nonadaptive group tests required to identify a given number of “non-defective” items from a large population containing a small number of “defective” items. In the asymptotic regime with the population size $N \rightarrow \infty$ , to identify $L$ nondefective items out of a population containing $K$ defective items, our results show that $\frac{C_s K}{1-o(1)} (\Phi (\alpha _0, \beta _0) + o(1))$ measurements are sufficient when the tests are reliable. Here, $C_s$ is a constant independent of $N, K$ , and $L$ , and $\Phi (\alpha _0, \beta _0)$ is a bounded function of $\alpha _0 \triangleq \lim _{N\rightarrow \infty } \frac{L}{N-K}$ and $\beta _0 \triangleq \lim _{N\rightarrow \infty } \frac{K}{N-K}$. In contrast, the necessary number of tests using the conventional approach of first identifying the $K$ defective items and picking the required number of nondefective items from the complement set grows with $N$ as $O\left(K \log N \right)$. We also derive upper bounds on the number of tests under both dilution and additive noise models. Our results are obtained under a very general sparse signal model, by virtue of which, they are also applicable to other important sparse signal based applications such as compressive sensing. [ABSTRACT FROM AUTHOR]

Capacity-achieving polar codes have gained significant attention in recent years. In general, polar codes can be decoded by either successive cancellation (SC) or the belief propagation (BP) algorithm. However, unlike SC decoders, performance optimizations for BP decoders have not been fully explored yet. In this paper, we explore novel early stopping criteria for polar BP decoding to significantly reduce energy dissipation and decoding latency. First, we propose two detection-type novel early stopping criteria for detecting valid outputs. For polar (1024, 512) codes, these two stopping criteria can reduce the number of iterations by up to 42.5% at 3.5 dB. Then, we propose a novel channel condition estimation approach, which can help select different stopping criteria in different SNR regions. Furthermore, the hardware architectures of polar BP decoders with the proposed stopping criteria are presented and developed. Synthesis results show that with the use of the proposed stopping criteria, the energy dissipation, and average latency of polar (1024, 512) BP decoder can be reduced by \10\% \!\sim\! \30\% with \2\% \!\sim\! \5\% hardware overhead, and average throughput can be increased by \20\% \!\sim\! \55\%. [ABSTRACT FROM PUBLISHER]

This paper introduces hardware architectures for encoding Quasi-Cyclic Low-Density Parity Check (QC-LDPC) codes. The proposed encoders are based on appropriate factorization and subsequent compression of involved matrices by means of a novel technique, which exploits features of recursively-constructed QC-LDPC codes. The particular approach derives to linear encoding time complexity and requires a constant number of clock cycles for the computation of parity bits for all the constructed codes of various lengths that stem from a common base matrix. The proposed architectures are flexible, as they are parameterized and can support multiple code rates and codes of different lengths simply by appropriate initialization of memories and determination of data bus widths. Implementation results show that the proposed encoding technique is more efficient for some LDPC codes than previously proposed solutions. Both serial and parallel architectures are proposed. Hardware instantiations of the proposed serial encoders demonstrate high throughput with low area complexity for code words of many thousand bits, achieving area reduction compared to prior art. Furthermore, parallelization is shown to efficiently support multi-Gbps solutions at the cost of moderate area increase. The proposed encoders are shown to outperform the current state-of-the-art in terms of throughput-area-ratio and area-time complexity by 10 to up to 80 times for codes of comparable error-correction strength. [ABSTRACT FROM PUBLISHER]

Low density parity-check (LDPC) codes, are widely used for error correction in digital communication systems. Their inclusion in communication standards requires to define decoders able to support efficiently a set of codes with different code length, code rates or code structures. In addition to this high flexibility, these decoders still have to achieve high throughputs in order to comply with standards requirements. In this paper, we propose to address the problem of designing generic and efficient LDPC decoders by using a nonsymmetric NISC-based architecture that performs layered decoding. NISC architectures provide flexibility with a limited loss in hardware efficiency. In addition, an automated design flow is used to efficiently assign computations to the processing units (PU) and to map data to the memory units (MU). Unlike previous works, the NISC decoder can include a number of PUs that is different than the number of MUs. This nonsymmetric characteristic provides a higher degree of freedom during the computation/data assignment phase of the design flow. This whole design framework automatically generates an LDPC decoder able to support any set of predetermined LDPC codes regardless of their parameters. The automated nature of the design framework enables to easily explore the design space for a given set of codes. Compared to state of the art LDPC decoders, the automatically generated decoders achieve higher hardware efficiency even for the challenging-to-implement unstructured LDPC codes. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Sphere Decoding (SD) is a highly effective detection technique for Multiple-Input Multiple-Output (MIMO) wireless communications receivers, offering quasi-optimal accuracy with relatively low computational complexity as compared to the ideal ML detector. Despite this, the computational demands of even low-complexity SD variants, such as Fixed Complexity SD (FSD), remains such that implementation on modern software-defined network equipment is a highly challenging process, and indeed real-time solutions for MIMO systems such as 4 \times 4 16-QAM 802.11n are unreported. This paper overcomes this barrier. By exploiting large-scale networks of fine-grained software-programmable processors on Field Programmable Gate Array (FPGA), a series of unique SD implementations are presented, culminating in the only single-chip, real-time quasi-optimal SD for 4 \times4 16-QAM 802.11n MIMO. Furthermore, it demonstrates that the high performance software-defined architectures which enable these implementations exhibit cost comparable to dedicated circuit architectures. [ABSTRACT FROM PUBLISHER]

In physical layer network coding, it has been argued that performance improvements of a few dBs can be achieved by decoding a linear combination of colliding frames at an intermediate relay. The best results have been achieved by directly decoding the necessary linear combination rather than decoding each of the colliding packets, but so far only rather impractical architectures have been proposed. We explore in this paper a more pragmatic approach, where some important problems have been solved. We show by simulation that the proposed system is able to outperform analog network coding, the only other known practical architecture in this area so far, with substantial gains especially when noise amplification at the relay can be particularly detrimental, for instance when spatial diversity is available or large networks are analyzed. [ABSTRACT FROM PUBLISHER]

In this paper, we jointly design architecture-aware (AA) low-density parity-check convolutional codes (LDPC-CCs) and the associated memory-based decoder architecture based on shuffled message-passing decoding (MPD). We propose a method for constructing AA-LDPC-CCs that can facilitate the design of a memory-based shuffled decoder using parallelization in both iteration and node dimensions. Through the use of shuffled MPD, the number of base processors and, hence, the decoder area is significantly reduced, since a fewer number of iterations is required in order to achieve a desired error performance. In addition, the use of memory instead of registers minimizes the implementation cost of each base processor. In the memory-based decoder, collisions in memory access can be avoided and the difficulty in exchanging information between iterations (processors) is overcome by using simple permutation networks. To demonstrate the feasibility of the proposed techniques, we constructed a time-varying (479, 3, 6) AA-LDPC-CC and implemented its associated shuffled decoder using a 90-nm CMOS process. This decoder comprises 11 processors, occupies an area of 5.36 , and achieves an information throughput of 1.025 Gbps at a clock frequency of 256.4 MHz based on post-layout results. [ABSTRACT FROM PUBLISHER]

Real-time data delivery over best-effort error-prone packet networks has invigorated the study of robust coding schemes, such as scalable multiple description coding (SMDC). In this context, the paper introduces a novel generic symmetric scalable multiple description quantizer (SSMDSQ) which generates perfectly balanced source descriptions. Novel embedded index assignments are proposed which are used to realize high, as well as medium-to-low redundancy SSMDSQs. Compared to existing designs, it is shown that the proposed quantizer constructions exhibit superior distortion-rate (D-R) performance. Moreover, this paper describes an innovative extension of the Lloyd-Max algorithm in order to optimize symmetric and asymmetric scalable multiple description quantizers. For a family of Generalized Gaussian (GG) source distributions, the proposed optimization algorithm yields on average a significant D-R performance gain over unoptimized quantizers. Furthermore, anchored in the designed SSMDSQs, an SMDC framework is established to realize packet-based transmission over erasure channels. In this framework, transmission strategies are determined for scenarios wherein the average packet loss rate over the transmission link is (a) unknown and (b) can be estimated at the encoder. For both scenarios, SMDC packetized transmission is simulated for a family of GG distributions. Experimental results confirm that, compared to contemporary schemes, the designed quantizer constructions (with or without optimization) account for a significant average gain in signal-to-noise ratio (SNR) for a wide range of packet loss rates. [ABSTRACT FROM PUBLISHER]

In collaborative estimation a network of nodes, each indirectly observing an underlying source, communicate with each other to form improved estimates of the underlying source. This paper derives an efficient collaborative estimation algorithm achieving high performance estimates with low communication and complexity utilizing tools from multiterminal source coding theory and modern practical coding theory. Relevant theoretical limits based on achievability from multiterminal source coding are first presented in order to characterize efficient tradeoffs between communication and estimation performance and inspire the architecture of the collaborative estimation code. Next low complexity tools from modern practical coding theory are utilized to get a practical collaborative estimation algorithm approaching these performance limits. The developed algorithm utilizes successively refined trellis coded quantization (SR-TCQ) to provide necessary diverse descriptions of the source, and low-density parity-check (LDPC) codes to provide an efficient means of compressing these descriptions for low complexity belief propagation decoding with side information. Comparing the communication versus estimate quality tradeoff performance attained by the developed low complexity scheme with that obtainable with an inner bound, an average distortion gap of only 1.0 and 1.6 dB at average rates of 3.32 and 2.33 b/s, respectively, is observed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how to send space-time codes with full diversity and low decoding complexity for X channels using precoders. First, we assume that we have 2 transmitters and 2 receivers. Each transmitter sends different codewords to each receiver at the same time. We propose an orthogonal transmission scheme that combines space-time block coding and array processing to achieve low-complexity decoding and full diversity for transmitted signals. To our best knowledge, this is the first scheme which can achieve low-complexity decoding and full diversity for any transmitted codeword in X channel when all the users transmit at the same time. Finally, we show that under certain conditions, our proposed scheme can be extended to a general case with any number of transmitters and receivers each with any number of antennas. Simulation results validate our theoretical analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we propose and analyze a generalized construction of distributed network codes for a network consisting of M users sending independent information to a common base station through independent block fading channels. The aim is to increase the diversity order of the system without reducing its throughput. The proposed scheme, called generalized dynamic-network codes (GDNC), is a generalization of the dynamic-network codes (DNC) recently proposed by Xiao and Skoglund. The design of the network codes that maximize the diversity order is recognized as equivalent to the design of linear block codes over a nonbinary finite field under the Hamming metric. We prove that adopting a systematic generator matrix of a maximum distance separable block code over a sufficiently large finite field as the network transfer matrix is a sufficient condition for full diversity order under link failure model. The proposed generalization offers a much better tradeoff between rate and diversity order compared to the DNC. An outage probability analysis showing the improved performance is carried out, and computer simulations results are shown to agree with the analytical results. [ABSTRACT FROM PUBLISHER]

This paper presents a randomized linear-dispersion space-time block code for decode-and-forward synchronous relays. The coding matrices are obtained as a set of columns (or rows) of randomly generated Haar-distributed unitary matrices. With respect to independent and identically distributed (i.i.d.)-generated codes, this particular isometric structure reduces the intersymbol interference generated within each relay. The gain over i.i.d. codes in terms of spectral efficiency is analyzed for both the LMMSE and the ML receivers under the assumption of frequency-flat quasi-static fading. In this setting, the spectral efficiency is a random quantity, since it depends on the random coding matrices. However, it is proven that the spectral efficiency converges in probability to a deterministic quantity when the dimensions of the matrices tend to infinity while keeping constant their ratio, i.e., the coding rate \alpha. Consequently, when the random coding matrices are large enough, the presented system behaves as a deterministic one. This result is achieved by means of the rectangular R-transform, a powerful tool of free probability theory which allows determining the distribution of the singular values of a sum of rectangular matrices. [ABSTRACT FROM PUBLISHER]

This paper considers transmission schemes in multiaccess relay networks (MARNs) where J single-antenna sources send independent information to one N-antenna destination through one M-antenna relay. For complexity considerations, we propose a linear framework, where the relay linearly transforms its received signals to generate the forwarded signals without decoding and the destination uses its multiantennas to fully decouple signals from different sources before decoding, by which the decoding complexity is linear in the number of sources. To achieve a high symbol rate, we first propose a scheme called ConcurrentS\rightarrowR\rightarrowD-IC_D in which all sources' information streams are concurrently transmitted in both the source-relay link and the relay-destination link. In this scheme, distributed space-time coding (DSTC) is applied at the relay, which satisfies the linear constraint. DSTC also allows the destination to conduct the zero-forcing interference cancellation (IC) scheme originally proposed for multiantenna systems to fully decouple signals from different sources. Our analysis shows that the symbol rate of ConcurrentS\rightarrowR\rightarrowD-IC_D is 1/2 symbols/source/channel use and the diversity gain of the scheme is upperbounded by M-J+1. To achieve a higher diversity gain, we propose another scheme called ConcurrentR\rightarrowD-IC_D in which the sources time-share the source-relay link. The relay coherently combines the signals on its antennas to maximize the signal-to-noise ratio (SNR) of each source, then concurrently forwards all sources' information. The destination performs zero-forcing IC. It is shown through both analysis and simulation that when N\geq 2J-1, ConcurrentR\rightarrowD-IC_D achieves the same maximum diversity gain as the full TDMA scheme in which information streams from each source are assigned to orthogonal channels in both links, but with a higher symbol rate. [ABSTRACT FROM AUTHOR]

Stochastic computation is a technique in which operations on probabilities are performed on random bit streams. Stochastic decoding of forward error-correction (FEC) codes is inspired by this technique. This paper extends the application of the stochastic decoding approach to the families of convolutional codes and turbo codes. It demonstrates that stochastic computation is a promising solution to improve the data throughput of turbo decoders with very simple implementations. Stochastic fully-parallel turbo decoders are shown to achieve the error correction performance of conventional a posteriori probability (APP) decoders. To our knowledge, this is the first stochastic turbo decoder which decodes a state-of-the-art turbo code. Additionally, an innovative systematic technique is proposed to cope with stochastic additions, responsible for the throughput bottleneck. [ABSTRACT FROM AUTHOR]

The blind maximum-likelihood (ML) detection of a general space-time block code (STBC) is considered a challenging implementation problem. Recent work has revealed that for the orthogonal STECs (OSTBCs), their special code structures can be exploited to formulate highly effective blind ML-based algorithms. Attracted by this realization merit, this paper investigates the blind ML identifiability of OSTBCs, with an emphasis on the binary PSK (BPSK) and quaternary PSK (QPSK) constellations. We find a class of OSTBCs, called the nonrotatable OSTBCs, that can be uniquely identified up to a sign (UIUTS) almost surely under a few mild assumptions. For example, for an independently distributed Rayleigh channel with any number of receiver antennas, a nonrotatable OSTBC can be UIUTS with probability 1. While this identifiability looks appealing already, we further examine a subclass of nonrotatable OSTBCs, called the nonintersecting subspace (NIS) OSTBCs. We prove that NIS-OSTBCs are UIUTS for any nonzero channel. However, NIS-OSTBCs are not available in the existing literature. To fill this gap, we devise a code construction procedure that can convert any (BPSK or QPSK) OSTBC to an NIS-OSTBC. [ABSTRACT FROM AUTHOR]

A novel quantization-based data-hiding method, called Rational Dither Modulation (RDM), is presented. This method retains most of the simplicity of the conventional dither modulation (DM) scheme, which is largely vulnerable to amplitude scalings and modifies it in such a way that the result becomes invariant to gain attacks. RDM is based on using a gain-invariant adaptive quantization step-size at both embedder and decoder. This causes the watermarked signal to be asymptotically stationary. Mathematical tools are used to determine the stationary probability density function, which is later used to assess the performance of RDM in Gaussian channels. It is also shown that by increasing the memory of the system, it is possible to asymptotically approach the performance of DM, still keeping invariance against gain attacks. RDM is compared with improved spread-spectrum methods, showing that the former can achieve much higher rates for the same bit error probability. Experimental results confirm the validity of the theoretical analyses given in the paper. Finally, a broader class of methods, that extends gain-invariance to quantization index modulation (QIM) methods, is also presented. [ABSTRACT FROM PUBLISHER]

Recently, low-density parity-check (LDPC) codes have attracts a lot of attention in the coding theory community. However, their real-world applications are still problematic mainly due to the Jack of effective decoder/encoder hardware design approaches. In this paper, we present a joint (3, k)-regular LDPC code and decoder/encoder design technique to construct a class of (3, k)-regular LDPC codes that not only have very good error-correcting capability but also exactly fit to high-speed partly parallel decoder and low-complexity encoder implementations. We also develop two techniques to further modify this joint design scheme to achieve more flexible tradeoffs between decoder hardware complexity and decoding speed. [ABSTRACT FROM AUTHOR]

Sparse superposition codes (SSCs) are capacity achieving codes whose decoding process is a linear sensing problem. Decoding approaches thus exploit the approximate message passing algorithm, which has been proven to be effective in compressing sensing. Previous work from the authors has evaluated the error correction performance of SSCs under finite precision and finite code length. This paper proposes the first SSC encoder and decoder architectures in the literature. The architectures are parametrized and applicable to all SSCs: A set of wide-ranging case studies is then considered, and code-specific approximations, along with implementation results in 65 nm CMOS technology, are then provided. The encoding process can be carried out with low power consumption ( \leq \text2.103 mW), while the semi-parallel decoder architecture can reach a throughput of 1.3 Gb/s with a $768\times 6$ -bit SSC codeword and an area occupation of 2.43 mm $^2$. [ABSTRACT FROM PUBLISHER]

The problem of robustly reconstructing a large number from its erroneous remainders with respect to several moduli, namely the robust remaindering problem, may occur in many applications including phase unwrapping, frequency detection from several undersampled waveforms, wireless sensor networks, etc. Assuming that the dynamic range of the large number is the maximal possible one, i.e., the least common multiple (lcm) of all the moduli, a method called robust Chinese remainder theorem for solving the robust remaindering problem has been recently proposed. In this paper, by relaxing the assumption that the dynamic range is fixed to be the lcm of all the moduli, a tradeoff between the dynamic range and the robustness bound for two-modular systems is studied. It basically says that a decrease in the dynamic range may lead to an increase of the robustness bound. We first obtain a general condition on the remainder errors and derive the exact dynamic range with a closed-form formula for the robustness to hold. We then propose simple closed-form reconstruction algorithms. Furthermore, the newly obtained two-modular results are applied to the robust reconstruction for multimodular systems and generalized to real numbers. Finally, some simulations are carried out to verify our proposed theoretical results. [ABSTRACT FROM PUBLISHER]

In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: 1) hybrid, where the signal processing operations are shared between nodes and APs; 2) centralized, where signal processing is entirely implemented at the APs; and 3) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches. [ABSTRACT FROM AUTHOR]

Polar codes are of great interests because they provably achieve the symmetric capacity of discrete memoryless channels with arbitrary input alphabet sizes while having an explicit construction. Most existing decoding algorithms of polar codes are based on bit-wise hard or soft decisions. In this paper, we propose symbol-decision successive cancellation (SC) and successive cancellation list (SCL) decoders for polar codes, which use symbol-wise hard or soft decisions for higher throughput or better error performance. First, we propose to use a recursive channel combination to calculate symbol-wise channel transition probabilities, which lead to symbol decisions. Our proposed recursive channel combination has lower complexity than simply combining bit-wise channel transition probabilities. The similarity between our proposed method and Arıkan's channel transformations also helps to share hardware resources between calculating bit- and symbol-wise channel transition probabilities. Second, a two-stage list pruning network is proposed to provide a trade-off between the error performance and the complexity of the symbol-decision SCL decoder. Third, since memory is a significant part of SCL decoders, we propose a pre-computation memory-saving technique to reduce memory requirement of an SCL decoder. Finally, to evaluate the throughput advantage of our symbol-decision decoders, we design an architecture based on a semi-parallel successive cancellation list decoder. In this architecture, different symbol sizes, sorting implementations, and message scheduling schemes are considered. Our synthesis results show that in terms of area efficiency, our symbol-decision SCL decoders outperform existing bit-decision and multi-bit SCL decoders. [ABSTRACT FROM PUBLISHER]

A low complexity, essentially-ML decoding technique for the Golden code and the three antenna Perfect code was introduced by Sirianunpiboon, Howard and Calderbank. Though no theoretical analysis of the decoder was given, the simulations showed that this decoding technique has almost maximum-likelihood (ML) performance. Inspired by this technique, in this paper we introduce two new low complexity decoders for Space-Time Block Codes (STBCs)—the Adaptive Conditional Zero-Forcing (ACZF) decoder and the ACZF decoder with successive interference cancellation (ACZF-SIC), which include as a special case the decoding technique of Sirianunpiboon We show that both ACZF and ACZF-SIC decoders are capable of achieving full-diversity, and we give a set of sufficient conditions for an STBC to give full-diversity with these decoders. We then show that the Golden code, the three and four antenna Perfect codes, the three antenna Threaded Algebraic Space-Time code and the four antenna rate 2 code of Srinath and Rajan are all full-diversity ACZF/ACZF-SIC decodable with complexity strictly less than that of their ML decoders. Simulations show that the proposed decoding method performs identical to ML decoding for all these five codes. These STBCs along with the proposed decoding algorithm have the least decoding complexity and best error performance among all known codes for N_t \leq 4 transmit antennas. We further provide a lower bound on the complexity of full-diversity ACZF/ACZF-SIC decoding. All the five codes listed above achieve this lower bound and hence are optimal in terms of minimizing the ACZF/ACZF-SIC decoding complexity. Both ACZF and ACZF-SIC decoders are amenable to sphere decoding implementation. [ABSTRACT FROM PUBLISHER]

Hybrid automatic repeat request (ARQ) protocols have become common in many packet transmission systems due to their incorporation in various standards. Hybrid-ARQ combines the normal ARQ method with forward error correction (FEC) codes to increase reliability and throughput. In this paper, we look at improving upon this performance using feedback information from the destination, in particular, using a powerful FEC code in conjunction with a proposed linear feedback code for the Rayleigh block fading channels. The new hybrid-ARQ scheme is initially developed for full received packet feedback in a point-to-point link. It is then extended to various multiple-antenna scenarios [e.g., multiple-input single-output (MISO), multiple-input multiple-output (MIMO), etc.] with varying amounts of packet feedback information. Simulations illustrate gains in throughput. [ABSTRACT FROM PUBLISHER]

In a multi-user wireless network equipped with multiple relay nodes, some relays are more intelligent than other relay nodes. The intelligent relays are able to gather channel state information, perform linear processing and forward signals whereas the dumb relays are only able to serve as amplifiers. As the dumb relays are oblivious to the source and destination nodes, the wireless network can be modeled as a relay network with smart instantaneous relay only: the signals of the source-destination link arrive at the same time as the source-relay-destination link. Recently, instantaneous relaying is shown to improve the degrees-of-freedom of the network as compared to the classical cut-set bound. In this paper, we study an achievable rate region and its boundary of the instantaneous interference relay channel in the scenario of (a) uninformed non-cooperative source-destination nodes (source and destination nodes are not aware of the existence of the relay and are non-cooperative) and (b) informed and cooperative source-destination nodes. Further, we examine the performance of interference neutralization: a relay strategy which is able to cancel interference signals at each destination node in the air. We observe that interference neutralization, although promised to achieve desired degrees-of-freedom, may not be feasible if the relay has limited power. Simulation results show that the optimal relay strategies improve the achievable rate region and provide better user-fairness in both uninformed non-cooperative and informed cooperative scenarios. [ABSTRACT FROM PUBLISHER]

Iterative processing is widely adopted nowadays in modern wireless receivers for advanced channel codes like turbo and LDPC codes. Extension of this principle with an additional iterative feedback loop to the demapping function has proven to provide substantial error performance gain. However, the adoption of iterative demodulation with turbo decoding is constrained by the additional implied implementation complexity, heavily impacting latency and power consumption. In this paper, we analyze the convergence speed of these combined two iterative processes in order to determine the exact required number of iterations at each level. Extrinsic information transfer (EXIT) charts are used for a thorough analysis at different modulation orders and code rates. An original iteration scheduling is proposed reducing two demapping iterations with reasonable performance loss of less than 0.15 dB. Analyzing and normalizing the computational and memory access complexity, which directly impact latency and power consumption, demonstrates the considerable gains of the proposed scheduling and the promising contributions of the proposed analysis. [ABSTRACT FROM PUBLISHER]