Integrated sensing and communication (ISAC) beamforming designs typically rely on the true sensing parameters which can hardly be achieved. In this work, we present a statistically robust ISAC beamforming design to combat the performance degradation caused by the random target and clutter parameter estimation errors. Specifically, our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR) averaged over angle and reflection coefficient estimation errors, while fulfilling the communication users’ SINR requirements. To solve this challenging stochastic optimization problem, we develop an efficient majorization-minimization (MM)-based algorithm. In particular, a semi-closed form solution is derived in each iteration for the single-user case. Simulation results show that, under the same communication constraints, our proposed algorithm outperforms the traditional non-robust designs in terms of the detection probability, and even approaches the clairvoyant scheme with known parameters.

We investigate the communication data enhanced direct position determination (DPD) for uplink multi-antenna orthogonal frequency division multiplexing (OFDM) systems. Different from prior related works, both pilot and unknown data signals are employed to estimate the positions of the mobile station (MS) and the virtual points corresponding to non-line-ofsight (NLOS) paths. To solve the novel localization problem, we propose a space alternating generalized expectation maximization (SAGE) based algorithm which regards the unknown data as latent variables. We further analyze the localization CramerRao bound (CRB) that incorporates the impact of communication data. Simulation results and the CRB analysis verify the remarkable localization accuracy improvement achieved by employing data signals. Moreover, the localization performance is also shown to approach the communication data aware CRB under relatively high signal-to-noise ratios (SNRs).

We consider the transmit waveform design for a collocated multiple-input multiple-output (MIMO) radar system, where one-bit analog-to-digital converters (ADCs) are deployed to enable a low-lost and power-efficient hardware implementation. Focusing on improving the target parameter estimation performance, we first derive a novel one-bit Cramér-Rao bound (CRB) metric by exploiting the Bussgang-based linear signal model and the worst-case Gaussian assumption. Then, based on the maximum likelihood principle, we develop a practical one-bit parameter estimation method to approach the derived CRB performance. Next, by minimizing the above one-bit CRB objective subject to a total power constraint, we formulate a transmit waveform optimization problem, which is highly nonconvex due to the one-bit quantization. To solve this problem, a majorization-minimization framework integrated with a projected gradient descent (MMPGD) algorithm is carefully designed whose convergence and complexity analysis are also provided. Finally, numerical results substantiate the tightness of the proposed one-bit CRB and the effectiveness of the MMPGD algorithm, where clear performance improvements can be achieved compared to the existing benchmark schemes.

In this paper, we study the transmit beamformer design to minimize the weighted sum finite blocklength (FBL) decoding error probability for a downlink multiuser multiple-input single-output (MISO) ultra reliable and low-latency communication (URLLC) system. Since the Gaussian Q-function in the design objective does not admit a closed form, the considered problem is challenging to solve. In order to handle the complicated problem, we first obtain its reformulation via a tight Gaussian Q-function approximation, which is then solved via the majorization-minimization (MM) technique tailored for the reformulated problem. Moreover, in order to further reduce the computational complexity, we first obtain the optimal structure of the transmit beamformer for the original problem, based on which we establish a neural network solution which avoids solving convex problems. Furthermore, we also investigate the extension to the statistically robust beamforming design with the imperfect channel state information (CSI). Simulation results verify the clear performance advantages of the proposed solutions over several state-of-the-art schemes as well as widely used beamforming schemes such as regularized zero-forcing (RZF) precoding in terms of the FBL decoding error probability. Moreover, the low-complexity solutions can effectively approach the proposed MM-based algorithms with much reduced computational costs.

Integrated sensing and communication (ISAC) enabled by millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technologies is envisioned to be a promising candidate for future wireless systems. In this paper, we study the mmWave massive MIMO aided ISAC transceiver design with one-bit digital-to-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) for reduced hardware complexity and power consumption. First, we develop a novel one-bit target detector based on the Bussgang decomposition, which fully exploits the spatial correlation at the receiver side for performance enhancement. Then, we derive the detection probability and the false alarm probability of the proposed detector in closed forms, from which we establish an interesting relationship between the detection performance and a new signal-to-quantization-plus-interference-plus-noise ratio (SQINR) metric. Furthermore, we formulate a one-bit ISAC transceiver optimization problem by incorporating both the communication mean-squared error (MSE) and proposed sensing SQINR metrics into the objective function. To address the complicated discrete optimization, we propose an efficient alternating optimization framework embedded with a majorization-minimization (AOMM) algorithm with guaranteed convergence. Finally, extensive simulations are conducted which confirm the validity of our one-bit detection performance analysis and show that the proposed detector outperforms a recent solution relying on the low input signal-to-noise/interference-to-noise ratio (LIS) assumption. Moreover, the proposed ISAC transceiver design can achieve excellent communication and sensing performances with acceptable complexity.

In this paper, we focus on the decoding error probability based beamforming design for a downlink multiantenna multiuser ultra reliable and low-latency communication (URLLC) system. We first optimize the transmit beamformer to minimize the maximum finite blocklength (FBL) decoding error probability under block fading channels, which turns out to be a complicated nonconvex problem with a non-closed-form objective function. A successive convex approximation (SCA)-based algorithm is developed to determine a high-quality solution. Moreover, in order to reduce the computational complexity of the proposed algorithm, we perform the downlink beamforming optimization by solving a simpler virtual uplink problem. The complexity of the resultant solution only scales linearly with the number of transmit antennas. Furthermore, a special case with quasi-static channels is investigated, where the proposed SCA and the uplink-downlink duality based solutions for the block fading channel can be simplified in a non-trivial manner. Simulation results verify the performance superiorities of the proposed algorithms in the context of short packet communication. In particular, the proposed designs outperform conventional regularized zero-forcing (RZF) and max-min signal-to-interference-plus-noise ratio (SINR) beamforming by evident gains in terms of the FBL decoding error probability.

Batched network codes (BNCs) are a low-complexity solution for communication through networks with packet loss. Although their belief propagation (BP) performance is proved to approach capacity in the asymptotic regime, there is no evidence indicating that their BP performance is equally good in the finite-length regime. In this paper, we propose a protograph-based construction for BNCs, referred to as protograph-based BNCs (P-BNCs), which significantly differs from existing BNCs in three aspects: 1) The vast majority of existing construction methods mainly focus on the degree distribution of check nodes (CNs), whereas P-BNCs not only specify the degree distributions of CNs and variable nodes (VNs) but also partially constrain the connectivity between CNs and VNs. 2) Traditional BNCs use a fixed degree distribution to generate all batches, making their performance highly sensitive to channel conditions, but P-BNCs achieve good performance under varying channel conditions due to their rate-compatible structures. 3) The construction of P-BNCs takes into account joint BP decoding with a sparse precode, whereas traditional constructions typically do not consider a precode, or assume the presence of a precode that can recover a certain fraction of erasures. Due to these three improvements, P-BNCs not only have higher achievable rates under varying channel conditions, but more importantly, their BP performance is significantly improved at practical lengths.

Batched sparse (BATS) codes were proposed as a reliable communication solution for networks with packet loss. In the finite-length regime, the error probability of BATS codes under belief propagation (BP) decoding has been studied in the literature and can be analyzed by recursive formulae. However, all existing analyses have not considered precoding or have treated the BATS code and the precode as two separate entities. In this paper, we analyze the word-wise error probability of finite-length BATS codes with a precode under joint decoding, including BP decoding and maximum-likelihood (ML) decoding. The joint BP decoder performs peeling decoding on a joint Tanner graph constructed from both the BATS and the precode Tanner graphs, and the joint ML decoder solves a single linear system with all linear constraints implied by the BATS code and the precode. We derive closed-form upper bounds on the error probability for both decoders. Specifically, low-density parity-check (LDPC) precodes are used for BP decoding, and any generic precode can be used for ML decoding. Even for BATS codes without a precode, the derived upper bound for BP decoding is more accurate than the approximate recursive formula, and easier to compute than the exact recursive formula. The accuracy of the two upper bounds has been verified by many simulation results. Based on the two upper bounds, we formulate an optimization problem to optimize the degree distribution of LDPC-precoded BATS codes, which improves BP performance, ML performance, or both. In our experiments, to transmit 128 packets over a line network with packet loss, the optimized LDPC-precoded BATS codes reduce the transmission overhead to less than 50% of that of standard BATS codes under comparable decoding complexity constraints.

Cost-effective phase noise (PN) compensation is essential for orthogonal frequency division multiplexing (OFDM) systems, especially in millimeter-wave (mmWave) communications. In this paper, we propose an intelligent semi-blind receiver design for PN-affected OFDM systems. Specifically, we first formulate a joint channel estimation, PN compensation, and data detection problem in the time domain, accounting for the constant modulus constraint of PN and discrete constraint of the data. To address the constrained non-convex problem, a low-complexity receiver based on the alternating direction method of multipliers (ADMM) and minorization-maximization (MM) is proposed. Subsequently, by adopting the deep unfolding technique, we present an intelligent receiver to avoid cumbersome parameter selection and also accelerate the algorithm convergence. Simulation results validate the clear performance superiority of our proposed design over existing schemes. Moreover, the complexity per iteration/layer is as low as O(N log2N) with N being the number of subcarriers.

This paper investigates the joint design of transmit waveform and receive filter for a collocated multipleinput multiple-output (MIMO) radar, where one-bit digitalto-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) are employed to reduce the hardware cost and power consumption. We first derive a novel signal-toquantization-plus-interference-plus-noise ratio (SQINR) metric via a careful theoretical analysis to accurately characterize the one-bit MIMO radar performance. Then, we formulate a onebit transceiver optimization problem by maximizing the SQINR objective subject to binary DAC constraints. To handle the complicated mixed-integer problem, we propose an efficient penalty-based majorization-minimization (PMM) algorithm with guaranteed convergence. As validated via simulations, the proposed algorithm achieves a noticeable performance gain over the existing benchmark scheme based on a low input signal-to-noise/interference-to-noise ratio (LIS) assumption.

Non-rectangular QAM can achieve better bit error rate performance in the face of different channel characteristics, and finding the constellation distribution that best matches the channel characteristics has always been a topic of discussion. The NVI$D$IA/Sionna open-source library provides a scheme of end-to-end transmission comprised of trainable constellation and deep neural network detection to achieve accurate information transfer. However, this application has a high computational complexity in the detection part, which contains dense layers that involve intensive and massive matrix multiplication operations. This study considers using an approximate matrix multiplication (AMM) algorithm based on table lookup, Multiply-ADDitioN-lESS (MADDNESS), to replace the accurate multiplication calculations in the DNN-Based detection and achieves a significant reduction in overall computational complexity at the cost of certain storage.

The minimum mean-squared error (MMSE) detector is widely used in massive multiple-input multiple-output communication systems for its suboptimal performance and simple deployment. However, the computational complexity of the MMSE detector is extremely high due to its matrix calculation when the number of antennas increases. In this paper, a learning vector quantization method is introduced to significantly accelerate specific matrix multiplication at the expense of acceptable performance decay and memory cost. Specifically, exact matrix multiplication is replaced by a series of simple operations involving comparison, querying the lookup table (LUT) and integer additions. The LUT saving the results of vector multiplication is constructed offline. When performing matrix multiplication online for signal detection, received signals are encoded into address indices for querying the LUT. Numerical results prove that the proposed method can achieve similar performance compared to exact matrix calculation for the MMSE detector with much lower computational complexity and acceptable memory cost.

Phase noise introduced by imperfect oscillators can degrade the performance of multiple-input multiple-output (MIMO) systems. To address the issue, this paper investigates a joint estimation of channel and phase noise algorithm by taking advantage of the soft output of the decoder. Specifically, a novel criterion is proposed to select the most reliable symbols as virtual pilots to refine the channel amplitude. Furthermore, we develop a decoding-aided extended Kalman filter (EKF) to track the phase variation. Numerical results demonstrate that the proposed approach can effectively improve the accuracy of the channel and phase noise estimation, thereby enhancing the bit error rate (BER) performance of the MIMO system.

In this paper, we study a class of staircase codes (SCCs), which is constructed by algebraic and single-parity-check component codes. These codes are referred to as mixed-component SCCs (MC-SCCs). We propose a low-complexity ternary message passing decoding algorithm for MC-SCCs, where error-and-erasure decoding plays a core role and soft reliability is reconstructed by hard decisions and channel log-likelihood ratios. We develop the density evolution analysis that is applicable to both binary and non-binary algebraic codes to optimize the decoder. By properly selecting mixed components, we can construct low-error-floor MC-SCCs with a smaller size of decoding window (proportional to component lengths) and a faster speed of convergence compared with the conventional SCCs. Moreover, we verify that for various requirements of overhead and decoding window size, MC-SCCs can outperform the conventional SCCs with both BCH components in terms of both error-rate performance and decoding complexity.

Reconfigurable intelligent surface (RIS) technology is emerging as a promising technique for performance enhancement for next-generation wireless networks. This paper investigates the physical layer security of an RIS-assisted multiple-antenna communication system in the presence of random spatially distributed eavesdroppers. The RIS-to-ground channels are assumed to experience Rician fading. Using stochastic geometry, exact distributions of the received signal-to-noise-ratios (SNRs) at the legitimate user and the eavesdroppers located according to a Poisson point process (PPP) are derived, and closed-form expressions for the secrecy outage probability (SOP) and the ergodic secrecy capacity (ESC) are obtained to provide insightful guidelines for system design. First, the secrecy diversity order is obtained as 2/α2, where α2denotes the path loss exponent of the RIS-to-ground links. Then, it is revealed that the secrecy performance is mainly affected by the number of RIS reflecting elements,N, and the impact of the number of transmit antennas and transmit power at the base station is marginal. In addition, when the locations of the randomly located eavesdroppers are unknown, deploying the RIS closer to the legitimate user rather than to the base station is shown to be more efficient. Moreover, it is also found that the density of randomly located eavesdroppers, λe, has an additive effect on the asymptotic ESC performance given by log2(1/λe). Finally, numerical simulations are conducted to verify the accuracy of these theoretical observations.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximuma posteriori(MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

A novel joint phase noise compensation and signal detection algorithm based on deep learning (DL) is proposed for orthogonal frequency division multiplexing (OFDM) systems under rapidly time-varying channels. Specifically, we first develop an alternating direction method of multipliers (ADMM) based solution to the difficult mixed-integer maximum likelihood (ML) estimation, along with a low-complexity ADMM solution that exploits the inherent feature of the inter-carrier interference (ICI). Furthermore, by unfolding the ADMM iterations, we present a model-driven network with trainable variables, which avoids cumbersome parameter search and also results in faster convergence. Simulation results demonstrate that our proposed DL-aided ADMM algorithms significantly outperform existing baselines in terms of BER performance with acceptable complexity.

We study the resource allocation for an uplink ultra reliable and low-latency communication (URLLC) system. The receive beamformer at the base station (BS) and the transmit power of users are jointly optimized to minimize the finite block-length (FBL) decoding error probability. To solve the difficult nonconvex problem, we first acquire a tractable reformulation by approximating the Gaussian Q-function. Although the resultant problem is still nonconvex, we propose an efficient algorithm where the optimal receive beamformer is obtained in closed form and the transmit powers of users are optimized by employing the majorization-minimization (MM) technique. Simulation results and complexity analysis verify the superiority of the proposed algorithm over existing benchmark schemes.

Interferences between uplink communication and downlink radar sensing can severely impair the performance of the time-division duplex (TDD) multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) system. In this paper, we propose an efficient joint downlink and uplink design to address this issue. Specifically, we jointly optimize the downlink radar sensing waveform and the uplink power allocation to minimize the weighted sum of sensing and communication mean squared errors (MSEs). Despite the non-convexity of the joint downlink and uplink design problem, we develop a majorization-minimization (MM) based algorithm to determine an efficient solution. The proposed algorithm is proved to be convergent. Moreover, as validated via simulation results, the proposed joint design can effectively mitigate the interference between uplink communication and radar sensing and provide remarkable performance gains over conventional separate design.

The capacity region for single input single out broadcast channel (SISO BC) is achieved by non-orthogonal multiple access (NOMA), which utilizes successive interference cancellation (SIC) to mitigate the inter-user interference. However, the complexity of SIC is high. To balance between the sum rate performance and the complexity of receivers, in this paper, we explore rate splitting multiple access (RSMA) in visible light communications (VLC). We formulate the joint rate allocation and power control problem to maximize the sum-rate under both quality of service (QoS) and SIC constraints. To solve this non-convex problem, a successive convex approximation (SCA) based algorithm is proposed to obtain a local optimal solution. Numerical results show that 1-layer RSMA is able to achieve very close performance to NOMA with much reduced complexity. Jointly considering the performance and complexity of the system, 1-layer RSMA is an attractive alternative to NOMA in SISO VLC networks.

Visible light communication (VLC) emerges as a promising technology for the explosively growing wireless services and demands. However, the system performance is severely impaired by the inherent nonlinearity of the VLC channel. In existing studies, the Hammerstein and Wiener models are widely used and often assumed for VLC channels due to the simple structure and low complexity. Yet, their effectiveness remains unclear and controversial. This work aims to figure out which one between the Hammerstein and Wiener models is more suitable for characterizing the VLC channel. We first design a single-tone test for qualitative analysis and further conduct an experiment based on multi-level pseudorandom sequences for quantitative evaluation. From the two well-designed experiments, we obtain a consistent conclusion that the Hammerstein model is more proper for describing the VLC nonlinearity and also more effective for post-distortion in VLC systems.

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.

This paper presents a product coding scheme based on binary images of Reed-Solomon (RS) codes and single-parity-check (SPC) codes for high-speed communications. Utilizing the special selection of components, we propose hybrid soft- and hard-decision iterative decoding (ID) algorithms for these codes, during which many RS component decoders can be early terminated. The proposed hybrid ID only exchanges ternary messages between component decoders and early stops the RS component decoders satisfying a proposed stopping criterion, thus low hardware and computational complexities are guaranteed. The hybrid ID of RS-SPC product codes not only performs comparable to the soft-decision decoding of low-density parity-check (LDPC) codes and block turbo codes (BTCs), but also takes much lower computational complexity and smaller decoder data flow. We also propose semi-analytical methods to estimate the waterfall and error-floor performances of the proposed scheme, which yield relatively accurate estimated results. The simulation results and error-floor estimations indicate that some good RS-SPC product codes have very low error floors that satisfy the requirements of optical systems.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

Reconfigurable intelligent surfaces (RISs) can establish favorable wireless environments to combat the severe attenuation and blockages in millimeter-wave (mmWave) bands. However, to achieve the optimal enhancement of performance, the instantaneous channel state information (CSI) needs to be estimated at the cost of a large overhead that scales with the number of RIS elements and the number of users. In this paper, we design a quasi-static broad coverage at the RIS with the reduced overhead based on the statistical CSI. We propose a design framework to synthesize the power pattern reflected by the RIS that meets the customized requirements of broad coverage. For the communication of broadcast channels, we generalize the broad coverage of the single transmit stream to the scenario of multiple streams. Moreover, we employ the quasi-static broad coverage for a multiuser orthogonal frequency division multiplexing access (OFDMA) system, and derive the analytical expression of the downlink rate, which is proved to increase logarithmically with the power gain reflected by the RIS. By taking into account the overhead of channel estimation, the proposed quasi-static broad coverage even outperforms the design method that optimizes the RIS phases using the instantaneous CSI. Numerical simulations are conducted to verify these observations.

This paper presents a ternary message passing (TMP) decoding algorithm for product codes constructed from binary image Reed-Solomon (RS) codes and single-parity-check (SPC) codes. All exchanged messages among component decoders in TMP decoding take value from a ternary alphabet {–1, 0, +1}, which gives a potential for designing fast decoders. In particular, intersymbol interference (ISI) channels are considered due to their applications in many high-speed systems. Moreover, we propose the density evolution (DE) analysis for RS-SPC product codes over AWGN and ISI channels. The DE analyses and simulation results show RS-SPC product codes under TMP decoding performs well in various channels.

In this paper, we focus on how to further improve the performance of short block-length low-density parity-check (LDPC) codes, especially the short LDPC codes in 5th generation (5G) New Radio (NR) systems. We propose an enhanced adaptive belief propagation (ABP) decoding algorithm with the assistance of partial cyclic redundancy check (CRC) bits. Meanwhile, the error detection ability can still be guaranteed by the remaining CRC bits and adaptive decoder parameters. Moreover, the proposed partial CRC-aided ABP (CA-ABP) decoding can be combined with the conventional BP decoding to further improve the performance. Simulation results show that compared with the BP decoding our proposed decoding method achieves an improvement in the error-correction performance.

In this paper, we propose a modified adaptive belief propagation (ABP) algorithm, which is referred to as the random ABP (R-ABP) algorithm, for decoding short channel codes adopted in the fifth generation (5G) New Radio (NR) wireless systems. Based on the cyclic redundancy check (CRC) concatenation structure of 5G channel codes, we can take partial or even all CRC bits into iterative R-ABP decoding to improve the error correction performance, while the error detection capability can still be guaranteed by the remaining CRC bits and a proposed threshold-based acceptance criterion. We call this improved R-ABP decoding the threshold-and-CRC-aided R-ABP (TCA-R-ABP) decoding. The simulation results show that our proposed TCA-R-ABP algorithm outperforms the state-of-the-art decoding algorithms for many 5G low-density parity-check (LDPC) and polar codes. Moreover, the proposed TCA-R-ABP algorithm leads to a unified decoder for LDPC and polar codes, which has the potential to be a lower-complexity decoder over the combination of the belief propagation (BP) and CRC-aided successive cancellation list (CA-SCL) decoders.

This paper presents a generalized construction of RS-SPC product codes. A low-complexity joint-decoding scheme is proposed for these codes, in which a BP-based iterative decoding is performed based on the binary expansion of the whole parity-check matrix. Various powerful RS codes can be used as the component codes for RS-SPC product codes, which gives a good performance for local decoding (decode a single component codeword). The proposed BP-based iterative decoding is a global decoding, and it achieves an error-correcting capability compa-rable to codes of large blocklengths. This two-phase decoding scheme preserves the low decoding latency and complexity of the local decoding while achieves high reliability through the global decoding. The complexity of the proposed iterative decoding is discussed, and the simulation results show the proposed scheme offers a good trade-off between the complexity and the error performance.

Coarse quantization by using low-resolution analog-to-digital converters (ADCs) is an attractive approach to relieve the burden of power consumption and hardware cost of implementing massive multiple-input multiple-output (MIMO) systems. In this article, we analyze the uplink spectral efficiency of a multiuser massive MIMO system with low-resolution ADCs in the context of orthogonal frequency division multiplexing (OFDM) under multipath channels. Firstly, we develop an efficient pilot scheme which results in a constant average power of the quantization noise for different channel delay power spectrums and also minimizes the mean squared error of channel estimation. Then a tight approximation of the uplink achievable rate is derived in a closed form considering both perfect channel state information (CSI) and estimated CSI. Then we analyze the impact of multipath channels on the system performance. Under perfect CSI, we discover that an increment of multipath taps has a positive impact on compensating the performance degradation due to the quantization noise. Under imperfect CSI, the most beneficial channel is uniformly distributed over a specific number of taps. Simulations are conducted to verify our analytical results.

In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications.

In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP). In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) when the multipath delay spread exceeding the length of CP. To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Inspired by the dependency between the adjacent subcarriers, a computationally efficient joint detection scheme is developed. Employing the proposed equalizer, an iterative receiver is also constructed and the detection performance is evaluated through simulations over measured multipath channels. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.

In this paper, a deep learning (DL) approach for enhancing the bit-interleaved coded modulation (BICM) receiver is designed to mitigate the clipping distortion in the low-density parity-check (LDPC) coded direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) systems. This work aims to combine the neural network (NN) with the physical layer communications by using a model-driven DL architecture. We first develop a non-iterative NN-aided BICM (NN-BICM) receiver, where the NN is trained with the loss function of cross-entropy to output the modified conditional probability through the softmax activation function, thereby assisting in a log-likelihood ratio (LLR) improvement. Then, we propose two iterative NN-BICM receivers for iterative demapping and decoding. The single iterative design feeds the soft decisions from the LDPC decoder back to the demapper only, while the joint iterative design feeds the soft decisions back to the demapper and NN jointly. By adopting the iteration-wise pre-training strategy, the joint iterative design has been improved by representing the intractable relationship between the conditional probability and the a priori probability with a deeper NN architecture. We further investigate an efficient bit loading algorithm for DCO-OFDM systems employing the NN-BICM receiver. Both NN-BICM receivers and iterative schemes can obtain remarkable performance gains over the existing benchmarks.

Multi-color visible light communication (MC-VLC) is able to achieve high date rate via multiple color channels. To separate different colors, optical filters have to be adopted. In conventional MC-VLC systems, the center wavelength (CWL) and bandwidth (BW) of optical filters is carefully designed for each transmitted color component at normal angles of incidence (AOIs). However, at non-normal AOIs, the CWL of optical filters shifts to shorter wavelength namely the filter CWL shift, leading to serious cross-color interference. In this paper, we propose a novel optical filter bank structure, which consists of a series of narrow-band optical filters, to address the issues caused by the CWL shift. We first establish the corresponding channel model for the VLC links based on optical filter bank, and then design the optimal combining receiver for different AOIs. Simulation results demonstrate that the proposed optical filter bank structure exhibits high and stable performance, and shows strong compatibility for different transmitter configurations.

In visible light communications (VLC), a neural network-aided bit-interleaved coded modulation (NN-BICM) receiver is designed to mitigate the clipping distortion for the LDPC coded DC biased optical orthogonal frequency division multiplexing (DCO-OFDM) systems. Taking the cross-entropy as loss function, the feed forward network is trained by backprop-agation algorithm to output the condition probability through the softmax activation function, thereby assisting in a modified log-likelihood ratio (LLR) improvement. This feed-forward network simplifies the input layer with a single symbol and the corresponding Gaussian variance instead of focusing on the inter-carrier interference over multiple subcarriers. To achieve a further gain, a staked NN-BICM (S-NN-BICM) iterative receiver is proposed by calculating the condition probability with the aid of apriori probability that derived from the extrinsic LLRs in the LDPC decoder at the previous iteration. Both NN-BICM and S-NN-BICM schemes achieve significant performance gains over the existing counterparts, especially more than 0.8 and 1.4 dB in the case of 16-QAM and 511 occupied subcarriers.

In this paper, we propose a coding scheme for distributed matrix-vector multiplication that builds upon the Luby transform (LT) codes with feedback. The ideal soliton distribution is utilized in our LT coding scheme to encode the sub-matrices. Besides, the belief propagation (BP) decoding algorithm is modified to cooperate with the feedback information. Compared with other coded distributed computations with straggling servers, our approach achieves lower computation latency when the overall delay incurred by the encoding, mapping and decoding process is considered. Furthermore, we compare the storage loads of different schemes and show that the LT coding with feedback has a strong comparative advantage in these straggler-tolerant computation scenarios.

In this paper, we propose a practical deep neural network for OFDM symbol equalization and demonstrate its advantages in combating large Doppler Shift. In particular, a novel zero-forcing initialized neural architecture named Cascaded Net (CN) is proposed for equalization, where deep trainable network is cascaded behind a zero-forcing preprocessor to prevent the network getting stuck in a saddle point or a local minimum point. In addition, we propose a sliding equalization approach to detect those OFDM symbols with large number of subcarriers. We also evaluate this novel equalizer, as well as the sliding algorithm, using Rayleigh fading channel with large Doppler shift. The numerical results show this novel equalizer can achieve better performance than zero-forcing equalizer and classical ICI cancellation methods in SISO scenario. Thanks to proper training methods, this equalizer is relatively robust to traditional methods when channel estimation is inaccurate or Doppler shift changes.

A novel iterative receiver for polar-coded massive multiuser multiple-input-multiple-output (MU-MIMO) systems is proposed. In the receiver, the feedbacks of the polar decoder, including the correctly decoded data and the soft outputs generated using Pyndiah's Chase algorithm, are available to the detector and the channel estimator, respectively. Moreover, interference cancellation (IC) schemes are employed to improve the overall performance and reduce the computation complexity. Simulation results show that the proposed receiver with polar codes can bring about considerable performance gains through iterations and can always outperform the one with LDPC codes.

Non-orthogonal multiple access (NOMA) relies on power domain multiplexing via successive interference cancellation (SIC). Current NOMA system designs are based on information rate and assume perfect SIC. However, error propagation in SIC is inevitable in practice. This paper considers imperfect SIC and investigates the NOMA system design from the perspective of minimum error probability. Specifically, we consider the uncoded and the coded NOMA systems and derive the error probabilities of the users. Then, we aim to minimize the average error probability of the users by searching for appropriate power allocation. Numerical results are provided to evaluate the minimum error performance of NOMA along with useful insights.

In this study, the authors propose a parallel concatenated decoding (PCD) scheme for short non‐binary low‐density‐parity‐check (NB‐LDPC) codes. It consists of a reduced trellis extended min‐sum (R‐TEMS) algorithm which efficiently reduces the check node updating complexity over high‐order Galois fields and a low complexity ordered statistic decoding aided by partial cyclic redundancy check bits. The R‐TEMS algorithm has a negligible performance loss and noticeable reduction in decoding complexity with respect to the trellis‐based EMS (TEMS) algorithm. Besides, the PCD shows a close error‐correction performance when compared with other concatenated decoding schemes for NB‐LDPC codes. It achieves a coding gain about 0.5 dB with reasonable complexity contrasted with the Q‐ary sum–product algorithm for high‐rate short NB‐LDPC codes over GF(256).

In visible light communication (VLC), using multiple colors is an efficient way to enhance data rate, leading to multi-color VLC (MC-VLC). However, the performance of MC-VLC is jeopardized by the spectral overlaps of different colors. Thin-film optical filters, as the key component of MC-VLC systems, are usually adopted to separate colors. The passband bandwidth (BW) and center wavelength (CWL) of optical filters are critical to mitigate the crosstalk among colors and, thus, must be carefully designed. Moreover, due to the intrinsic wavelength shift of the CWL with the varying of the angle of incidence, it is challenging to support mobility for MC-VLC. In this paper, we consider a joint design of multiple optical filters for MC-VLC by properly selecting the BW and CWL of each filter. We first investigate the optical filter design for a fixed receiver location. Then, to support mobility, we propose two robust optical filter designs, namely, statistically and worst case robust designs, which do not rely on the exact receiver location. Efficient methods are developed to solve the corresponding design problems and obtain the optimized optical filters. Compared with the existing optical filters, the proposed optical filters exhibits much better performance in various scenarios.

The tradeoff between narrow beams and low training overhead is deemed to a bottleneck for millimeter-wave (mmWave) systems. Particularly, in multi-user (MU) scenarios, the training overhead grows linearly with the number of users. To address this issue, this paper proposes a codebook based simultaneous beam training scheme for mmWave MU systems with split/partially-connected structures. In this scheme, the multi-resolution codebook based hierarchical beam searching is adopted to reduce the training overhead and acquire the angle of departure (AoD) for each user-group. Meanwhile, the concurrent beams, offered by the corresponding subarray-groups of split structures, enable simultaneous beam training for all served user-groups. Consequently, the overall training overhead is significantly reduced. Moreover, a two-phase training protocol is elegantly built, where the whole array is used to reap large array gain in the initial phase. Furthermore, in the subsequent parallel training phase, multiple subarray-groups conduct simultaneous beam training for the corresponding user-groups. Hence, advantages on both low training overhead and large array gain are achieved. Numerical results show the proposed scheme outperforms traditional counterparts, especially in moderate-high signal-to-noise ratio (SNR) regimes with limited transmission blocks.

Hierarchical beam training methods are imposed with some limitations on antenna spacing, the number of antennas and/or radio-frequency (RF) chains, which can not be directly extended to general deployments. To address these issues, this paper investigates a coordinated subarray based beam training scheme for indoor sub-Terahertz (sub-THz) multi-user (MU) communications with split hybrid structures. The capability of concurrent beams offered by multiple subarrays in the hybrid structure is exploited to partition the whole angle domain into multiple orthogonal subregions on the basis of subarrays, and each subregion is exclusively covered with a corresponding subarray without explicit interference among them, such that these multiple narrow beams can be simultaneously shaped in their reduced subregions. Consequently, an increased success rate of angle of departure (AoD) estimation can be obtained with affordable overhead in typical deployments, without limitation on the number of antennas in each subarray. Analysis and simulation results verify the feasibility and the superiority of the proposed scheme.

Pulse blanking is a common approach for impulsive interference mitigation in orthogonal frequency division multiplexing (OFDM) systems. The blanking threshold that decides whether a received signal is blanked or not, shows a crucial effect on the mitigation performance. Considering the influence of error correcting codes, we investigate the threshold optimization of pulse blanking for low-density parity-check (LDPC) coded OFDM systems. The protograph based extrinsic information transfer (PEXIT) charts and system simulations are used to tackle the optimization problem of blanking threshold with various LDPC coding schemes, which include different proto-graph structures, code lengths, code rates and the number of iterations. The simulation results show that the optimal blanking threshold changes with the code rate, whereas it remains the same regardless of the protograghs, code lengths and decoding iterations.

Visible light communication (VLC) equipped with multiple light-emitting diodes (LEDs) can provide near ubiquitous indoor coverage for both communication and illumination. This paper introduces the concept of biased beamforming to explore the full potential of multicarrier multi-LED VLC systems. Biased beamforming includes two components to be jointly designed: a direct current (DC) bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact on clipping in multi-LED VLC systems. Next, we consider the joint optimization of beamforming and biasing to maximize data rate. We find the optimal beamformer and analytically characterize its structure. We further optimize the bias on each LED and provide the globally optimal solution in closed form for flat channels, which leads to several critical insights that are helpful for practical systems. We also derive a simplified near-optimal solution for dispersive channels and develop an efficient method for biased beamforming. The performance of the proposed biased beamforming design outperforms existing solutions.

As a promising technology for 5G networks, device-to-device (D2D) communication can improve spectrum utilization by sharing the resources of cellular users (CUs). However, this is at the cost of generating interference to the CUs. While most existing works focused on eliminating or suppressing the interference between the D2D links and the CUs, such interference could in fact be beneficial for improving the security of cellular communication. Specifically, D2D links may, in return for reusing cellular resources to achieve high spectral efficiency, act as friendly jammers and help the CUs against malicious wiretapping. To reach this win-win situation, D2D resource management has to be designed from a physical layer security perspective. In this paper, we consider the joint optimization of power allocation and channel assignment of the D2D links and the CUs with the aim to provide security to the CUs and improve the spectral efficiency of the D2D links simultaneously. We focus on the challenging downlink resource sharing problem and investigate both single-channel and multi-channel D2D communications. The resulting resource management design problems turn out to be difficult nonlinear mixed integer problems. Nevertheless, by exploiting the inherent properties of the formulated optimization problems, we are able to analytically characterize the optimal power allocation of the CUs and D2D links, and develop efficient methods for joint optimization of their channel assignments. Simulation results show that the proposed resource management policies outperform several baseline schemes and can indeed achieve the desired twofold objective.

In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully connected structure. It is revealed that increasing signal power can compensate for the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully connected structure in either massive MIMO or low signal-to-noise ratio scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mm-wave) channels verify the conclusions above.

In this paper, we study the hybrid precoding structures for massive multiuser multiple-input multiple-output (MIMO) systems. Particularly, the practical hardware network models with insertion loss are developed. The achievable rates of two typical hybrid precoding structures, the fully-connected structure and the sub- connected structure, are investigated, from which we discover that the sub-connected structure always outperforms the fully-connected structure in terms of the achievable rate in massive MIMO. We further characterize the effect of quantized analog precoding which indicates that the subconnected structure is able to achieve better performance with fewer feedback bits than the fully-connected structure. We also propose a channel statistics-based codebook used for the digital precoding stage which is more suitable for hybrid precoding systems than the conventional random vector quantization (RVQ) codebook.

In multi-color visible light communications (MC-VLC), using multiple colors is an efficient way to enhance data rate, which is, however, jeopardized by the spectral overlaps of colors. Thin-film interference filters, as the key components of MC-VLC, are usually adopted to separate color channels. A careful design of center wavelength (CWL) and bandwidth of the filter passband is critical to mitigate crosstalk among colors, especially when the color number is greater than three. Moreover, due to the intrinsic short-wavelength shift of CWL with the increase of angle of incident (AoI), it is also challenging to support the mobility for MC-VLC employing thin-film interference filters. In this work, we investigate the thin-film interference filter design for two scenarios: the receiver is at a fixed and known position with perfect knowledge of AoI, and the receiver randomly locates with only a stochastic knowledge of AoI. To maximize the signal to interference plus noise ratio (SINR) for each color channel, an alternative optimization algorithm is proposed. Numerical simulations illustrate that, in both scenarios, our designed filters could achieve a higher SINR and a lower bit error rate than existing filters. Moreover, compared with the filter for fixed AoI, our filter for random AoI exhibits a balanced performance in mobile scenarios.

This paper proposes a novel concept of biased beamforming to explore the full potential of multicarrier visible light communication (VLC) systems equipped with multiple light-emitting diodes (LEDs). Biased beamforming requires joint optimization of both: a direct current bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact of clipping in multi-LED multicarrier systems, before carrying out the joint optimization of beamforming and biasing. We derive the optimal biased beamforming in closed form, along with several critical insights that are helpful for practical systems. The proposed biased beamforming strategy outperforms existing solutions, as shown by our simulation results.

In a visible light communication (VLC) system, the nonlinearity of LED is one of the challenges that prevents reliable communication. In order to mitigate the nonlinearity of LED, in this paper, the concept of spatial summing pulse amplitude modulation (PAM) is developed where PAM signals are separated into several on#x002F;off keying (OOK) signals transmitted by different LED groups. The signal streams from different LED groups are designed to sum in space into a single signal stream which is detected by a conventional optimal maximum likelihood detector. A scheme is proposed to cope with the distortion caused by the mismatch among the LED array in the spatial summing PAM system and the error performance of the spatial summing PAM is analyzed. Simulation results verify that the proposed scheme can remarkably improve the error performance.

To obtain a robust physical‐layer network coding (PLNC) for two‐way relaying system, the authors propose a low‐density parity‐check (LDPC) coded PLNC scheme with phase pre‐rotation to combat the carrier phase asynchronous in the multiple access stage. At two users, each modulation symbol is rotated according to a predefined incremental phase without the knowledge of carrier phase difference. Decoding simulation and numerical analysis are carried out to evaluate this joint LDPC and PLNC scheme with phase pre‐rotation. Simulation results show that this strategy can achieve a significant improvement and a good trade‐off between performance and complexity. Protograph‐based extrinsic information transfer analysis demonstrates the proposed scheme is robust, which is consistent with the simulation results.

White light-emitting diode (LED) consisting of red, green, blue, and amber chips (RGBA-LED) has recently been adopted as transmitter in visible light communication (VLC) systems. This paper proposed a reshaped orthogonal frequency division multiplexing (R- OFDM) scheme for RGBA-LED-based VLC systems. In the R- OFDM, the signal is adjusted by separation and biasing after clipping (BAC) operations for transmitting in RGBA-LED. Then, the biasing factor of BAC is derived according to the color mix ratio (CMR) of RGBA-LED. At the receiver, We develop a direct detection algorithm to recover the original data, and analyze its the theoretical bit error rate (BER). Furthermore, a lower bound of the BER is analyzed under high clipping ratio (CR) by using SNR upper bound. Finally, simulation results confirm our theoretical analysis and verify that R-OFDM outperforms the ACO-OFDM in term of BER.

We study transceiver optimization in the context of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying with source-destination link. In particular, by applying the known results of the optimal destination receiver and relay precoder, we mainly focus on the source beamformer optimization which manifests itself as a difficult non-convex problem. To find a globally optimal solution to this problem, we first transform it into an equivalent solvable convex problem via semidefinite relaxation (SDR). Inspired by the SDR based approach, we conduct a further analysis and successfully derive a novel semi-closed form solution to the optimal source beamformer. It is interesting to observe that the solution possesses a generalized eigenmode transmission structure depending on a weighted sum of the Gram matrices of both source-relay and source-destination channels.

In this paper, we investigate a multiuser MIMO downlink with imperfect channel state information (CSI) from the physical-layer security provision. In a classical transmitter (Alice)-legitimate receiver (Bob)-eavesdropper (Eve) model using artificial noise to disturb Eve's reception, we consider a general scenario where all nodes are equipped with multiple antennas, and Bob's CSI is acquired by pilot-assisted channel estimation. For designing the secure transmit beamforming, we utilize random matrix quantization (RMQ) to quantize Bob's channel estimate, and then Bob feeds it back to Alice. Due to the effects of imperfect CSI at Alice, the secrecy performance is upper bounded in the high signal-to-noise ratio (SNR) regions. In order to avoid the interference-limited phenomenon, we present a scaled strategy for the secrecy system utilizing derived upper bound on the secrecy rate loss. Moreover, it is shown that our derived results can be easily extended to a special case where both legitimate and eavesdropping receivers equip a single antenna each, where random vector quantization (RVQ) is utilized instead. By employing our proposed feedback strategy, the secrecy rate increases with transmit power, and the certain secrecy requirement can be guaranteed.

This paper considers a visible light system that serves the dual purpose of illumination and communication. We maximize the joint offset and power optimization in direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communications (VLC). We take both the optical and electrical power constraints into account. We analytically characterize the optimal solutions along with new insights on setting the direct current (DC) offset and information-carrying power in DCO-OFDM systems. We also investigate the relationship and impact of the optical and electrical power constraints, along with numerical results.

In this letter, we study physical-layer secure transmission for a multi-antenna relay system in the presence of multiple eavesdroppers, with emphasis on quality-of-service (QoS) based optimization. In particular, our ultimate goal is to minimize relay transmit power and meanwhile guarantee the fulfillment of QoS constraints with regard to both legitimate receiver and eavesdroppers. Instead of directly dealing with this intricate problem, we first prove that the optimal relay precoder possesses a generalized channel matching structure, which allows us to transform the original problem into a tractable semi-definite programming (SDP). We then rigorously verify the optimality of this approach, and further discuss its extension to the scenario with only channel statistics. Simulations are finally performed to demonstrate the superiority of the proposed optimal design.

In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.

This paper studies adaptive coordinated control of device-to-device (D2D) communications underlaying a cellular network with a multi-antenna base station (BS). We analyze the performance of cellular and D2D users under the channel imperfections including both quantization error and feedback delay. More specifically, we derive the received signal-to-interference-and-noise ratio (SINR) distributions of both D2D and cellular users. Based upon this, we further obtain a closed-form approximation of the achievable rate, which serves as a theoretical support for choosing transmit beamforming strategies adaptively.

We study the robust transceiver design for amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems with a direct link and imperfect channel state information (CSI). Under this circumstance, we aim at obtaining the globally optimal transceiver that minimizes the mean-squared error (MSE) of symbol detection. Specifically, given a source beamformer, we first derive closed-form expressions for the optimal relay precoder and destination receiver. Then, to tackle the intricate non-convex problem with respect to source beamformer, we develop an efficient approach involving one-dimensional search and semidefinite programming (SDP). We prove rigourously that the proposed method yields a globally optimal solution. Simulation results verify the pronounced performance provided by the proposed design.

The full potential of Device-to-device (D2D) communication relies on efficient resource reuse strategies including power control and matching of D2D links and cellular users (CUs). This letter investigates downlink resource reuse between multiple D2D links and multiple CUs. Our goal is to achieve a network utility enhancement for D2D communication while ensuring the QoS of the CUs. Despite the combinatorial nature of the problem and the coupled power constraints, we characterize the optimal D2D-CU matching as well as their power coordination, and propose an efficient algorithm to jointly optimize all D2D links and CUs. The proposed downlink resource reuse strategy shows a superiority over existing D2D schemes.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. We analyze the achievable rate loss due to channel state information (CSI) mismatch arising from both channel estimation and quantization feedback. We first derive an upper bound to characterize the effect of imperfect CSI, and then present a limited feedback strategy for both the two-hop channels in the relay system. This newly presented feedback strategy reveals the relationship among the CSI quantization levels, the transmit power, and the pilots for channel estimation. An optimized pilot design at the base station and the relay is also presented by applying the derived bounds for the two-hop system.

Transceiver designs have been a key issue in guaranteeing the performance of multiple-input multiple-output (MIMO) relay systems, which are, however, often subject to imperfect channel state information (CSI). In this paper, we aim to design a robust MIMO transceiver for nonregenerative MIMO relay systems against imperfect CSI from a worst-case robust perspective. Specifically, we formulate the robust transceiver design, under the minimum mean-squared error (MMSE) criterion, as a minimax problem. Then, by decomposing the minimax problem into two subproblems with respect to the relay precoder and destination equalizer, respectively, we show that the optimal solution to each subproblem has a favorable channel-diagonalizing structure under some mild conditions. Based on this finding, we transform the two complex-matrix subproblems into their equivalent scalar forms, both of which are proven to be convex and can be efficiently solved by our proposed methods. We further propose an alternating algorithm to jointly optimize the precoder and equalizer that only requires scalar operations. Finally, the effectiveness of the proposed robust design is verified by simulation results.

Resource sharing between primary users (PUs) and secondary users (SUs) in cognitive radio (CR) networks is built on strict interference limitations. However, such limitations may be easily violated by SUs using imperfect SU-to-PU channel state information (CSI). In this paper, we propose a robust decentralized CR network design by explicitly taking into account imperfect SU-to-PU CSI from a game theoretical perspective. We formulate the CR network design as a generalized Nash equilibrium problem (GNEP), where the SUs compete with each other over the resources made available by the PUs, who are protected by the robust aggregate interference constraints. We establish a framework-based generalized variational inequality (GVI) theory to analyze the formulated robust GNEP. It is shown that the solution to the robust GNEP can be obtained by solving a GVI, which can be addressed by a distributed pricing mechanism in the CR network, where the SUs play a priced NEP with given prices and the PUs are in charge of setting prices. Then, we propose distributed algorithms, along with their convergence properties, for the SUs to solve the priced NEP and for the PUs to update prices, respectively. We also provide an efficient method to compute the optimal transmit strategy of each SU via convex optimization.

In this paper, we investigate the performance enhanced transceiver design in a Device-to-Device (D2D) communication system underlaying a cellular network. The problem of joint transmitter and receiver optimization via maximizing the entire D2D and cellular transmission rate is considered. Due to the non-convexity of the primal problem, we resort to decomposing the problem into a sequence of standard convex quadratic programs. An iterative sequential optimization algorithm is accordingly presented for joint transceiver design at both the base station and the device terminals. Computer simulations convinced the performance enhancement of our proposed scheme compared with conventional transmission schemes.

For practical bi-directional relaying, symbols transmitted by two sources cannot arrive at the relay with perfect symbol and frame alignments and the asynchronous multiple-access channel (MAC) should be seriously considered. In this paper, we consider the Low-Density Parity-Check (LDPC)-coded BPSK signalling over the general asynchronous MAC with both frame and symbol misalignments. For the symbol-asynchronous MAC, we present a formal log-domain generalized sum-product-algorithm (Log-G-SPA) for efficient decoding. When the frame-asynchronism is encountered at the relay, we propose an original approach by employing the cyclic LDPC codes and the simple cyclic-redundancy-check (CRC) coding technique. Simulation results demonstrate the effectiveness of the proposed approach.

Hybrid ARQ transmissions are effective means for on-demand error correction in wireless networks against unpredictable channel distortions and interferences. By requesting retransmission of failed data packets upon detecting packet errors, receivers can integrate original and retransmitted packet signals for more effective packet detection and error correction. Often, only a small fraction of the packet bits is in error. For channels with sufficiently high capacity such as in MIMO wireless systems, it is typically unnecessary to reuse the entire channel for hybrid-ARQ retransmissions in order to correct the few remaining errors. To improve the wireless channel utility, we propose and investigate a shared hybrid ARQ (SHARQ) mechanism in MIMO systems where failed data can piggyback on new data frames over the diversity MIMO channel. To guarantee certain quality of HARQ retransmission, we design a linear transmit precoder based on channel state information at the transmitter and receiver to achieve a predetermined {minimum} signal-to-noise ratio (SNR) gain for the SHARQ signals by minimizing the transmission power. Our proposed SHARQ system takes advantage of a simple successive interference cancellation receiver to facilitate a simpler and better transmitter.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. Compared with the system using perfect channel state information (CSI), we analyze the achievable rate loss due to CSI mismatch arising from both channel estimation and CSI quantization feedback. An upper bound to the rate loss is firstly derived to characterize the effect of imperfect CSI. Given the rate loss bound, we then present a limited feedback strategy for both two-hop channels in the relay system to guarantee a bounded rate loss for growing SNRs. This newly presented strategy reveals the relationship between CSI quantization level and other system parameters, including the transmit power and the pilots for channel estimation, on the entire two-hop system. Finally, computer simulations are carried out for verifying the correctness of our derived results.

In this paper, we develop a novel joint transmit and receive design for multiuser MIMO (MU-MIMO) systems. The proposed scheme exploits the channel temporal correlation and incorporates the matched filter (MF) combining vector into the beamforming design. Considering the beamforming is optimized with the outdated channel state information (CSI) feedback, we design the beamformer by maximizing the conditional expectation of signal-to-leakage-and-noise ratio (SLNR) given that a delayed version of CSI feedback is available. In this way, the optimized beamforming is able to alleviate the performance degradation due to the CSI delay. Numerical results show that our proposed scheme achieves almost the same performance as conventional iterative algorithms but with much lower complexity.

The authors study the optimisation of the rate-compatible (RC)-irregular-repeat-accumulate (IRA) codes for asymmetric Slepian–Wolf coding to deal with flexible correlated sources. By assuming two different additive white Gaussian noise channels, the authors formulate a non-uniform extrinsic information transfer method to optimise the degree distribution of RC-IRA codes. The parity check matrices of RC-IRA codes are constructed from a mother matrix by combining puncturing and check-splitting, which achieves a good trade-off between the lowest-rate codes and the highest-rate codes. Simulation results show that the optimised non-uniform error-correcting code performs very close to the Slepian–Wolf limit over the target compression rate range and outperforms the prior results.

We study the performance of secure communications over correlated fading channels in the presence of an eavesdropper, where the main and eavesdropper channels are correlated. We derive exact expressions for both the average secrecy capacity and the outage probability in the form of infinite series. Moreover, the truncated error of the infinite series representations involved in the analytical results is also investigated. The accuracy of our performance analysis is verified by simulation results.

This paper investigates the beamforming design for multiuser downlink with limited feedback, where a practical channel state information (CSI) feedback model consisting both feedback delay and CSI quantization error is considered. Under this circumstance, we derive a closed-form multiuser beamforming scheme via minimizing the expected mean square error (MSE) with respect to the feedback imperfection. Compared with conventional MMSE beamforming scheme, we find that not only the regularization factor but the optimal MMSE beamforming structure changes due to the CSI imperfection, especially the feedback delay. Numerical results verify in different cases that the optimized beamforming design outperforms conventional ones in terms of both sum rate and BER performance.

Interference alignment has emerged as new tool for achieving high capacity in multi-user and multi-channel wireless interference channels. Based on maximizing a degree of freedom (DOF) metric defined for asymptotic signal to noise ratio (SNR), interference alignment encounters limitations in practical applications because of limited SNR. In addition, perfect interference alignment requires strict antenna-user number condition which may not always hold true as user number increases. In this work, we address these two practical issues in interference alignment. We propose a new definition of interference alignment metric designed to assess the effectiveness of non-ideal interference alignment. We apply the new interference alignment metric to investigate the "best effort" interference alignment in multi-user OFDM systems with limited SNR and limited dimension (subcarriers).

Physical layer secret key can be extracted from wireless fading channels based on channel quantization with guardband (CQG). In this paper, we analyze the key generation efficiency of the CQG protocol. Analytical closed-form expressions of bit error rate (BER) and key generation efficiency are derived for the CQG protocol. In order to further improve the key generation efficiency, we concatenate the CQG protocol with reconciliation. And then, we present a upper bound of key generation efficiency, which can be maximized by careful selecting the guardband region and LDPC codes. The key generation efficiency is also analyzed by considering the attacks in the case of an unauthenticated wireless channel.

Based on the recently-proposed iterative receiver by Colavolpe et al., we extend it to further incorporate the mechanism of frequency offset compensation. As a progressive approach, the factor graph of a long frame is first divided into numerous subgraphs of short block, iterative decoding can successfully start its work as the effect of frequency offset can be well mitigated due to the small size of subgraph. As the iteration goes on, the extrinsic information from decoder can be employed to get finer estimate of the frequency offset. The system performance can be steadily improved as the block size is progressively expanded to the final frame size.

In this paper, both the least square (LS) and the low rank linear minimum mean square error (Lr-LMMSE) frequency-domain channel estimation algorithms based on pilot frequency division multiplexing are proposed for the distributed space-frequency (SF) coded cooperative communication system with multiple relay nodes, where both the channels from the source node to the relay nodes and from the relay nodes to the destination node are characterized by frequency-selective fading. Theoretical analysis shows that the proposed algorithms achieve all the frequency-domain channel fading coefficients of the multi-relay distributed SF coded cooperative communication system successfully and avoided the superposition of the pilot symbols transmitted by different relay nodes. Simulation results confirm the performance of the proposed channel estimation algorithms, including the high accuracy, extraordinary practicality and the low complexity.

In this paper, we show how to compute the Slepian-Wolf lower bound for the reconciliation of physical layer secret keys between two radio terminals. Two coding approaches, including the syndrome method and the parity method, have been proven to require the same number of bits exchanged between two terminals for the reconciliation in the noiseless environment. We also present a practical coding approach based on LDPC codes, and the gap between the practical approach and the Slepian-Wolf bound is given.

We investigate the problem of downlink user scheduling in multiple-input multiple-output (MIMO) systems withMtransmit antennas andKhomogeneous multiantenna receiving users. We develop a simple algorithm to schedule a subset ofMactive users for data transmission. We first identify a set ofLcandidate users that should provide channel information feedback. We then selectMactive users among theLcandidates based on joint consideration of their effective channel gains and directions. We derive an asymptotic upper bound for the sum rate gap between the maximum sum rate of theMselected active users, achieved by dirty paper coding, and the full sum capacity of the original MIMO broadcast channel. Utilizing the upper bound, we show that the sum rate gap can be reduced to meet user requirement by suitably choosing a large but still finiteL. Furthermore, we also investigate the performance of the low complexity zero-forcing beamforming (ZFBF) for theMactive users.

The authors study the problem of limited channel information feedback in multiple-input single-output (MISO) broadcast channels serving heterogeneous users. The heterogeneous users have different feedback channel capacities because of their physical constraints and limitations. Our objective is to design an optimised limited feedback strategy for multiple users by using a set of multi-resolution codebooks such that, under varying and quantised channel information feedback, the multiuser system can maximise its achievable sum rate. By exploiting the receive antenna combining technique, the authors further generalise the proposed scheme to the multiuser beamforming case with multi-antenna users. Finally, the authors verify the proposed scheme by numerical methods. Simulation results show that the sum rate performance for heterogeneous users can be effectively improved by using the proposed limited feedback scheme, which is optimised according to only statistic channel information of users.

A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds.

We investigate the design of a limited feedback mechanism for multiple-input-multiple-output (MIMO) broadcast systems that are equipped with automatic repeat request (ARQ) capabilities. Given multiple antennas at the base-station (BS) and single antenna subscribers, we focus on maximizing the ergodic system sum capacity and propose an adaptive feedback bit allocation scheme under the constraint of total feedback bandwidth. Furthermore, we develop two efficient feedback allocation schemes based on asymptotic analysis. We present simulation results that verify the effectiveness of the proposed adaptive allocation schemes.

The queued-code based on low-density parity-check (LDPC) codes, which exploit both the near Shannon-limit performance LDPC codes and the instantaneous channel state information (CSI), can provide excellent performance in the block fading channels. The existing work utilized the conventional random puncturing and repetition operation, which may lead to considerable performance loss due to the changed intrinsic connections of parity-check matrix (PCM) for the receiver. In this paper, we present a reversed sequential puncturing (RSP) scheme and a construction method with modified dual-diagonal structure for the LDPC coded queued-code. The consecutive puncturing pattern in reversed direction can ensure the uniform row-weight distribution of the punctured PCM. The modified dual-diagonal form and the RSP scheme can ensure no more than one punctured symbol involved in each check-sum equation. The simulation results demonstrate that our proposed scheme can achieve noticeable performance gains.

The clustering of cycles to form stopping sets are first observed by T. Tian and et al. As the determination of stopping sets of minimum size is NP-hard, we propose to consider clustering of cycles to avoid the stopping sets of small sizes. In particular, the clustering of two cycles are considered for an improved version of progressive edge-growth construction of LDPC codes.

In this paper, a modification to the weighted bit- flipping (WBF) decoding algorithm for low-density parity-check (LDPC) codes is proposed. This modification, based on the adjustment in reliabilities of the check sums during iterative decoding, can be applied to various WBF algorithms. Our studies show that the modification can be achieved with a small increase in complexity, but leads to an appealing improvement in error performance.

We investigate new scheduling algorithms for MIMO broadcast systems to maximize the achievable sum rate of block diagonalization transceivers. In contrast to conventional schemes which select users directly, we investigate a more efficient type of scheduling algorithms, named as multiuser eigenmode transmission (MET). It is based on the selection of eigenmodes of all users. Utilizing an iterative procedure to calculate the Moore-Penrose generalized inverse of a matrix consisting of the already selected eigenmodes, we propose a low complexity multiuser eigenmode selection method based on the greedy algorithm. Then we show that the multiuser dominant eigenmode transmission can achieve the same sum rate scaling law as that of DPC. Therefore, to further reduce the complexity, we restrict our search space to the set of dominant eigenmodes and derive an alternative eigenmode selection algorithm with lower complexity at the cost of little performance loss, which we name as the low-complexity multiuser dominant eigenmode selection algorithm.

This paper investigates the scheduling of user signals in MIMO systems with spatially correlated channels. By maximizing an upper bound of the sum capacity, we propose a scheduling scheme which requires only a single scalar feedback from each receiving user. To further reduce the need for user information feedback and better explore the spatial correlation information, a more efficient scheduling method is also developed. This new approach only requires a 1-bit indicator from each user and selects users according to the slowly varying MIMO channel correlation information. Numerical results verify the effectiveness of our proposed schemes.

This work investigates the design of linear precoders for ARQ packet retransmissions in multi-input multi-output (MIMO) systems. We consider transmitter precoder design based on partial MIMO channel information in the form of their covariance feedback. Our objective is to maximize the ergodic mutual information provided by multiple (re)transmissions of a packet subject to transmission power constraint. We propose a set of near-optimal successive linear ARQ precoders for flat fading MIMO channels. This progressive linear ARQ precoder combines the appropriate power loading and the reverse-order pairing of singular values in the current retransmission with previous transmissions. This reverse-order pairing is a special feature unique to our sequential ARQ preceding approach with demonstrated performance gains.

The parity-check matrix of a quasi-cyclic low- density parity-check (QC-LDPC) code can be compactly represented by a polynomial parity-check matrix. By using this compact representation, we derive a necessary and sufficient condition for determining the girth of QC-LDPC codes in a systematic way. The new condition avoids an explicit enumeration of cycles for determining the girth of codes, and thus can be well employed to generate QC-LDPC codes with large girth.

We investigate the joint optimization of automatic repeat request (ARQ) and MIMO diversity for multi-user wireless communication systems. Exploiting MIMO for diversity gain and ARQ for error correction, our goal is to better integrate MIMO and ARQ in the transceivers to improve multi-user uplink performance. Specifically, we design optimal linear precoders for ARQ retransmissions by multiple wireless uplink terminals equipped with MIMO transmitters and we rely on joint detection of multiple retransmissions at the receivers. To better utilize ARQ diversity, we derive a set of progressive linear ARQ precoders for flat fading multiple-access channels based on the criterion of maximizing the mutual information of multiuser (re)-transmissions. We propose two efficient iterative numerical algorithms to determine the optimal precoders.

In this paper, we develop a two-stage hybrid channel estimation and ICI cancellation structure for OFDM systems. The double selective channels are approximated by an improved basis expansion model (BEM), which is more accurate than the conventional BEM when the normalized Doppler frequency is smaller than one. Based on this improved model, a new ICI cancellation scheme is proposed to reduce ICI impact. Simulation results show that the framework performs well.

A simplified method for algebraic soft- decision decoding of Reed-Solomon codes, which is based on the Chernoff bound algorithm proposed by El-Khamy and McEliece, is presented. This algorithm greatly reduces the computational complexity of the Chernoff bound algorithm while maintaining good performance. Different from previous front-end methods, which operate in the probability domain, the proposed algorithm achieves better performance by assigning multiplicity in the log probability domain.

This work investigates the adaptive resource allocation scheme for the downlink of multiuser OFDM systems. We focus on the problem of maximizing the overall spectral efficiency while maintaining the QoS requirements of users, including bit error rate and individual minimum rate requirements. Under the assumption of equal power allocation, an efficient algorithm is proposed to obtain the suboptimal solution of the resource allocation. In this algorithm, we first introduce some positive multipliers, one for each user, according to their minimum rate constraints (MRC), and then a parallel subcarrier-and-bit allocation scheme is designed using these multipliers with low complexity. The numerical results show that our algorithm not only substantially reduces the computational complexity of the existing algorithm, but also provides a noticeable performance improvement.

Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO), which destroys the orthogonality and causes inter-carrier interference (ICI). In this paper, we investigate the average error probability of OFDM systems impaired by CFO over frequency selective Rayleigh fading channels. By exploiting the Beaulieu series, the effects of CFO on the symbol error rate (SER) and bit error rate (BER) performance, which depend strongly on the statistical characteristics of the fading channels, are expressed as the sum of an infinite series in terms of the characteristic function (CHF) of the ICI. We only consider OFDM systems modulated with BPSK, QPSK, 8-PSK and 16-QAM, and the Gray-coded mapping are employed for the evaluation of BER performance.

A natural relationship between weighted bit-flipping (WBF) decoding and message-passing decoding is explored. This understanding can help us develop a dual WBF decoding algorithm from one type of message-passing decoding algorithm and vice versa. For min-sum decoding, one can find that its dual WBF algorithm is the algorithm proposed by Jiang et al. For belief-propagation (BP) decoding, we propose a new WBF algorithm and show its performance advantage. For some high-rate low-density parity-check (LDPC) codes of large row weight, it is shown that the WBF algorithm proposed by Liu and Pados performs extraordinarily well. However, its dual message- passing decoding does not work well. Furthermore, we propose a parallel implementation framework for various WBF algorithms. Compared to serial implementations, various WBF algorithms in their parallel form converge significantly faster and often perform better.

We propose a computationally efficient semidefinite relaxation (SDR) of the maximum likelihood (ML) detector for 16 quadrature amplitude modulation (16-QAM) in multiple-input multiple-output (MIMO) systems. The SDR stems from a variant of the ML problem, which utilizes set operation algorithms to formulate the alphabet constraint. Theoretical analysis and numerical simulations demonstrate that the proposed method can provide improved error performance as compared to some previous detectors.

In this paper, we propose maximum a posteriori probability (MAP) based equalizers for orthogonal frequency division multiplexing (OFDM) systems over time-varying multipath channels. Under the approximation of linear variation in an OFDM symbol, it is easy to obtain the equivalent channel matrix. Based on the Tailbiting trellis structure and the fact that intercarrier interference (ICI) energy is concentrated in adjacent subcarriers, a new Tailbiting BCJR equalizer, operating in the frequency domain, is developed. Furthermore, a Tailbiting DF- BCJR algorithm is proposed to achieve better performance, and an MBCJR algorithm is also introduced to reduce the implementation complexity. Simulation results indicate that, by successfully taking advantage of the diversity gain caused by time-selective channels, our equalizers demonstrate good performance in fast fading environments.

Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO), which destroys the orthogonality and causes inter-carrier interference (ICI). ICI self-cancellation schemes based on polynomial cancellation coding (PCC-OFDM) can evidently reduce the sensitivity of systems to CFO. In this paper, we analyze the performance of PCC-OFDM systems over additive white Gaussian noise (AWGN) channels in the presence of CFO. Two criteria are used to evaluate the effect of the CFO on performance degradation. Firstly, the closed-form expressions of the statistical average carrier-to-interference power ratio (CIR) and ICI power are presented. The closed-form expressions depend only on the normalized CFO, and are irrelevant to the number of subcarriers. Secondly, by exploiting the properties of the Beaulieu series, the effect of the CFO on symbol error rate (SER) for PCC-OFDM systems modulated with BPSK, QPSK and 16-QAM is exactly expressed as the sum of an infinite series in terms of the characteristic function (CHF) of the ICI.

Carrier frequency offset (CFO) estimation in an OFDM system is usually divided into fractional frequency offset (FFO) estimation and integer frequency offset (IFO) estimation. The former is generally done in the time domain while the latter in the frequency domain. We propose a maximum likelihood estimation algorithm for IFO estimation which works in the time domain using two FFOs with different estimation ranges. After introducing the concepts of frequency offset diagram, solution subset and minimum distance, we reveal the close relationship between the minimum distance and the error rate of the ML estimation result. Our further analysis exhibits a performance-range tradeoff which is tunable by the choice of the greatest common divisor (GCD) of two integers.

The parity-check matrix of a quasi-cyclic code can be represented by a polynomial parity-check matrix with a significantly lower dimension. By using this compact representation, we can develop an efficient method for locating the girth of the quasi-cyclic code. The proposed girth-locating algorithm can be well employed to generate quasi-cyclic low-density parity-check codes with large girth

The channel information is required in conventional V-BLAST algorithm. When the training sequences are short, the channel estimation is very noisy, which incurs the system performance degradation. We propose a semi-blind detection algorithm in this paper. Signals transmitted from different antennas are separated blindly first based on the statistical independence of the signals. Short training sequences are then utilized to eliminate the permutation and scale ambiguities which are inherent to blind separation algorithms. Simulation results demonstrate that the performance of the proposed algorithm is better than that of the conventional V-BLAST algorithm both in flat and frequency-selective fading channel when the training sequences are short.

In a conventional 2D RAKE receiver, the channel vector is usually estimated based on the LS criterion only using the pilot. When the power of the pilot is low or the variation of the channel is fast, the error of the channel estimation becomes large. We propose an improved algorithm in this paper. The steering vector of the desired signal is estimated firstly using a subspace decomposition method and a constrained condition is then configured employing this estimated steering vector. The channel vector is estimated based on the constrained LS criterion applying traffic signals. The pilot is only used to get the initial value of the estimation. The estimation accuracy of the channel vector can progressively increase through iteration. Simulation results demonstrate that the improved algorithm upgrades the estimation accuracy effectively.

A joint receiving scheme of RAKE combining and soft-output Viterbi equalization for low spreading factor wideband DS-CDMA systems is proposed in this paper. The equalization technique based on soft-output Viterbi algorithm (SOVA) is applied to symbol sequences after RAKE combining to effectively eliminate the multi-path interference (MPl) and inter-symbol interference (ISI) during transmission of high rate data in wideband DS-CDMA systems, which provides significant improvement over conventional RAKE receiver.