Massive Machine-Type Communication (mMTC) is a critical Internet of Things (IoT) scenario in 5G and beyond 5G (B5G) wireless networks, characterized by a vast number of devices, smaller data packets, sporadic transmission, and diverse latency requirements. Massive multiple-input-multiple-output (MIMO) technology allows multiple user equipments (UEs) to transmit their data simultaneously over the same resource block, making it a promising technology to support mMTC. However, when massive UEs attempt to access the massive MIMO network simultaneously, the network will experience severe overload. To address this challenge, we propose a Deep Deterministic Policy Gradient (DDPG)-based delay-aware Access Class Barring (ACB) dynamic access control scheme for mMTC in massive MIMO networks. In this scheme, we model the access blocking probability as a function of latency sensitivity for each active UE with a shared parameter, ensuring that the closer the current delay is to a UE’s delay budget, the higher the access priority of that UE. We then propose a DDPG-based algorithm to optimize the access blocking probability and the access blocking time in ACB. Simulation studies demonstrate that, compared with the baseline methods, the proposed scheme significantly increases the number of successful access UEs while maintaining access delays within the budget constraints.

An analytical framework integrating performance characterization and coding theory is proposed to mitigate sneak path (SP) interference in resistive random-access memory (ReRAM) crossbar arrays. The core innovation is identified in the mathematical decomposition of ReRAM’s non-ergodic data-dependent channel into multiple stationary memoryless subchannels. Through information-theoretic analysis, an approximate finite-length characterization of the theoretical lower bound for decoding word error probability (WEP) is established. This is achieved by systematically analyzing the SP occurrence rate in constrained array geometries combined with comprehensive evaluation of both mutual information and dispersion metrics across the decomposed channel components. Building upon this decomposition paradigm, a systematic code construction methodology is developed using density evolution principles for sparse-graph code design. The designed codes not only exhibit capacity-approaching decoding thresholds but also yield word error rate simulation results that are close to the derived WEP bound under practical crossbar configurations.

The integration of multicarrier modulation and multiple-input-multiple-output (MIMO) is critical for reliable transmission of wireless signals in complex environments, which significantly improve spectrum efficiency. Existing studies have shown that popular orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM) offer significant advantages over orthogonal frequency division multiplexing (OFDM) in uncoded doubly selective channels. However, it remains uncertain whether these benefits extend to coded systems. Meanwhile, the information-theoretic limit analysis of coded MIMO multicarrier systems and the corresponding low-complexity receiver design remain unclear. To overcome these challenges, this paper proposes a multi-slot cross-domain memory approximate message passing (MS-CD-MAMP) receiver as well as develops its information-theoretic (i.e., achievable rate) limit and optimal coding principle for MIMO-multicarrier modulation (e.g., OFDM, OTFS, and AFDM) systems. The proposed MS-CD-MAMP receiver can exploit not only the time domain channel sparsity for low complexity but also the corresponding symbol domain constellation constraints for performance enhancement. Meanwhile, limited by the high-dimensional complex state evolution (SE), a simplified single-input single-output variational SE is proposed to derive the achievable rate of MS-CD-MAMP and the optimal coding principle with the goal of maximizing the achievable rate. Numerical results show that coded MIMO-OFDM/OTFS/AFDM with MS-CD-MAMP achieve the same maximum achievable rate in doubly selective channels, whose finite-length performance with practical optimized low-density parity-check (LDPC) codes is only$0.5\sim 1.8$dB away from the associated theoretical limit, and has$0.8\sim 4.4$dB gain over the well-designed point-to-point LDPC codes.

Low Earth orbit (LEO) satellite networks are expected to support heterogeneous services, including enhanced mobile broadband (eMBB) communications, massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, existing coexistence schemes, such as puncturing and superposition, struggle to achieve an effective trade-off among reliability, latency, and spectral efficiency due to their limited degrees of freedom (DoF). To address this challenge, we propose a novel rate-splitting grant-free (RS-GF) transmission scheme that integrates rate-splitting multiple access (RSMA) with grant-free random access (GF-RA) to efficiently support heterogeneous quality of service (QoS) requirements. The high-rate eMBB user employs single-layer rate splitting (RS) over the entire slot, while short-packet Internet-of-Things (IoT) devices associated with URLLC and mMTC adopt GF-RA via single mini-slot transmissions. Building on this RS-GF framework, we analyze the outage performance of the proposed scheme. Specifically, we derive the average packet error probability (PEP) of IoT devices in the finite blocklength (FBL) regime and analyze the eMBB user’s outage probability under imperfect successive interference cancellation (SIC) and mini-slot collisions. On this basis, we present simplified analytical solutions for sparse and dense IoT deployment scenarios, and Monte Carlo simulations validate our analytical derivations. Simulation results demonstrate that the proposed RS-GF scheme outperforms state-of-the-art solutions for service coexistence in LEO satellite networks.

The sneak path (SP) issue presents a substantial challenge for resistive random-access memory (ReRAM), significantly affecting data storage reliability. The SP rate, which represents the proportion of memory cells impacted by SPs, is a crucial parameter influencing the probability of data detection errors. In this paper, we concentrate on analyzing the probability distribution of the SP rate in ReRAM arrays that incorporate imperfect selectors. Our research indicates that when ReRAM stores data following an independent and identically distributed (i.i.d.) Bernoulli distribution with parameter$q$, and the array size is large, the SP rate approximates a Gaussian distribution. The mean and variance of this distribution can be explicitly derived as functions of the number of selector failures, parameter$q$, and the array size.

Grant-free random access (GF-RA) has recently emerged to support massive random access. Due to the absence of access grant in GF-RA, the base station (BS) has to first identify each active user. However, the state-of-the-art user-activity detection (UAD) solution, i.e., covariance-based maximum-likelihood detection (CB-MLD) is still subject to some critical deficiencies. Specifically, the update step size in each CB-MLD iteration relies on an asymptotically large antenna number, which may cause convergence issues in practice. In addition, the hard-decision threshold for UAD remains to be fine-tuned in complicated scenarios. Both deficiencies are hard to address via analytical methods. Thus, we propose a hybrid-driven UAD network (HyD-UADNet), where a model-driven network is constructed to learn the proper update step size, and a data-driven network is designed to learn the soft decision on user activity. Furthermore, we construct a dynamic configuration-adaptive mixture-of-expert network (CA-MoENet). This CA-MoENet can adaptively produce weighting coefficients for different expert HyD-UADNets, so as to enhance the UAD robustness against varying configurations. Finally, simulations show the superior UAD accuracy of the HyD-UADNet, and reveal the robustness of the CA-MoENet even if the testing configuration is never seen by any expert during training.

An enhanced on-off division multiple access (ODMA) transmission scheme is introduced for unsourced multiple access networks. Building upon the foundational ODMA transmission scheme, we have implemented further refinements to the original joint on-off pattern and data detection algorithm. Specifically, we propose a pattern collision resolution technique that can blindly recognize the collision degree of each on-off pattern, and then iteratively recover the data of collided users over a joint factor graph. Furthermore, we introduce a finite-length performance analysis for on-off pattern detection and iterative multi-user decoding. Through this analysis, we derive numerous numerical results, revealing the impact of various parameters on the performance of collision degree detection and multi-user decoding, respectively. By summarizing the rules observed from these numerical results, we formulate design strategies for these parameters, aiming to optimize the overall performance of our scheme. The inherent super sparse property of ODMA ensures that our scheme maintains low decoding complexity. Numerical results demonstrate that, with the implementation of our pattern collision resolution method and meticulous parameter design, the proposed scheme achieves a gap of less than 1.2 dB compared to the random coding bound for up to 300 active users. This performance surpasses state-of-the-art schemes across a broad range of user numbers.

The achievable rate of code over resistive random-access memory (ReRAM) channel with finite selector failures was published in our recent work. The rate was derived under the assumption of independent and identically distributed (i.i.d.) input. In this work, focusing on the ReRAM channel with a single selector failure in the memory array, we derive an upper bound on achievable rate under arbitrary input distribution. This upper bound is within 0.02 bits from the achievable rate of i.i.d. input, indicating that i.i.d. is very close to optimal for large memory arrays. Moreover, we analyze the achievable rate of random code over ReRAM channel with suboptimal decodings where the decoder ignores the channel correlation. Our result indicates that in this case the achievable rate is limited by the capacity of a memoryless channel. We reveal both weak and strong asymptotic properties of ReRAM channel to prove this. The proof can be directly extended to the case of ReRAM with an arbitrary number of selector failures in the memory array.

Low earth orbit (LEO) satellite-based Internet of Things (IoT) has great potential to provide seamless global coverage, but the large propagation delay and severe Doppler shift in terrestrial-satellite link (TSL) will become the most challenging problem. To handle these challenges and facilitate massive grant-free random access, we propose an orthogonal time frequency space-based scramble-division multiple access (OTFS-SDMA) scheme, where the scrambling technique is used to tackle the correlated TSL channels between neighboring devices. At the receiver, we first propose a user activity detection (UAD) method based on capturing the dominant line-of-sight (LoS) path, without relying on the assumption of a static TSL. To facilitate accurate channel estimation (CE) against severe Doppler shifts, we exploit prior information about satellite velocity to detect the angles of arrival (AoAs) of active devices with the two-dimensional multiple signal classification (2D-MUSIC) algorithm, and further estimate the Doppler shifts. Building on the Doppler estimation, the orthogonal matching pursuit (OMP) algorithm is used to estimate the sparse TSL channel in the time-delay (TD) domain. In accordance with the OTFS-SDMA scheme, we propose a cross-domain elementary signal estimator (CD-ESE) for multi-user detection (MUD). In the CD-ESE MUD structure, both bit-level and symbol-level scrambling sequences help to distinguish neighboring active devices with correlated TSL channels, and the channel decoder works in conjunction with the CD-ESE to enhance MUD accuracy. Simulation results are provided to demonstrate the superior performance of the proposed OTFS-SDMA scheme over the state-of-the-art solutions.

An enhanced on-off division multiple access (ODMA) transmission scheme is proposed for unsourced multiple access network. The message of each active user is divided into two parts, where the first part is used to determine an on-off pattern, and the second part is encoded and transmitted in a time-hopping manner according to an on-off pattern. Leveraging the super sparse property of ODMA, the users' on-off pattern and pattern collisions are blindly detected based on the received signal without the help of pilot. Moreover, the on-off pattern detection, data decoding and collision recovery are performed iteratively over one sparse graph to enhance the overall system reliablity. We propose a finite-length performance analysis to the on-off pattern detection and iterative multi-user decoding, based on which both the user access sparsity, and channel code are optimized. Numerical result shows that with a rate 1/ 3 low-density parity-check code over G F (26), the gap between the proposed scheme and the random coding bound is less than 1.2 dB for up to 300 active users.

Massive Machine-Type Communications (mMTC) features a massive number of low-cost user equipments (UEs) with sparse activity. Tailor-made for these features, grant-free random access (GF-RA) serves as an efficient access solution for mMTC. In GF-RA systems, the covariance-based maximum likelihood detection (CB-MLD) algorithm is extensively employed to achieve the user-equipment activity detection (UAD). However, the limited receiving antennas and UAD decision made upon the hard threshold may undermine the UAD accuracy of the CB-MLD algorithm. To address this problem, we propose a hybrid-driven deep neural network (DNN) for UAD termed as the hybrid-driven user-equipment activity detection network (HyD-UADNet), which is composed of a model-driven coordinate descent network (MD-CDNet) and a data-driven soft thresholding network (DD-STNet). Specifically, the MD-CDNet is designed to modify the step size for the coordinate descent operation in each CB-MLD iteration and alleviate the impact of the limited antennas. Following the MD-CDNet, the DD-STNet is constructed as an adaptive soft thresholding function to replace the hard threshold for the UAD decision. Simulation results are provided to demonstrate the effectiveness of the hybrid-driven method and the performance of the HyD-UADNet.

Massive machine-type communications (mMTCs) features a massive number of low-cost user equipment (UE) with sparse activity. Tailor-made for these features, grant-free random access (GF-RA) serves as an efficient access solution for massive machine-type communication (mMTC). However, most existing GF-RA schemes rely on strict synchronization, which incurs excessive coordination burden for the low-cost UEs. In this work, we propose a receiver design for asynchronous GF-RA, and address the joint user-activity detection (UAD) and channel estimation (CE) problem in the presence of asynchronization-induced intersymbol interference. Specifically, the delay profile is exploited at the receiver to distinguish different UEs. However, a sample correlation problem in this receiver design impedes the factorization of the joint likelihood function, which complicates the UAD and CE problem. To address this correlation problem, we design a partially uni-directional (PUD) factor graph representation for the joint likelihood function. Building on this PUD factor graph, we further propose a PUD message passing-based sparse Bayesian learning (SBL) algorithm for asynchronous UAD and CE (PUDMP-SBL-aUADCE). Our theoretical analysis shows that the PUDMP-SBL-aUADCE algorithm exhibits higher signal-to-interference-and-noise ratio (SINR) in the asynchronous case than in the synchronous case, i.e., the proposed receiver design can exploit asynchronization to suppress multiuser interference. In addition, considering potential timing error from the low-cost UEs, we investigate the impacts of imperfect delay profile, and reveal the advantages of adopting the SBL method in this case. Finally, extensive simulation results are provided to demonstrate the performance of the PUDMP-SBL-aUADCE algorithm.

This paper investigates the generalized linear system (GLS), widely employed to evaluate the impact of nonlinear preprocessing on wireless transceivers. Two state-of-the-art signal recovery algorithms, namely generalized approximate message passing (GAMP) and generalized orthogonal/vector AMP (GOAMP/GVAMP), are comparatively studied. They have demonstrated Bayesian optimality for independently and identically distributed (IID) Gaussian matrices and unitary matrices, respectively. However, Bayesian optimality does not inherently guarantee error-free signal recovery. For coded GLS, the information-theoretic (i.e., achievable rate) limit of GAMP remains unknown, and there are still no analytical comparisons between GAMP and GOAMP/GVAMP in terms of the mean-square error and information-theoretic limit. To address these issues, we present the achievable rate analysis and optimal coding principle for GAMP with IID Gaussian matrices, as well as provide comprehensive comparisons with GOAMP/GVAMP with unitary matrices. Specifically, based on the celebrated I-MMSE lemma and the preconditions for state evolution (SE) to hold, the simplified variational SEs of GAMP and GOAMP/GVAMP are derived, leveraging the IID and unitary matrix properties to analyze the achievable rate and optimal coding principle, respectively. On this basis, it is proven that GOAMP/GVAMP outperforms GAMP in terms of asymptotic MSE and maximum achievable rate while requiring less complexity. Furthermore, two common nonlinear functions, clipping and quantization, are used as examples to demonstrate the theoretical comparisons and practical low-density parity-check (LDPC) code design for GAMP and GOAMP/GVAMP. Numerical results show that GAMP and GOAMP/GVAMP with optimized LDPC codes can approach the theoretical limits within 0:3 dB and overcome the decoding deterioration and even divergence of the existing state-of-the-art methods, particularly under low-resolution quantization.

To support complex communication scenarios in next-generation wireless communications, this paper focuses on a generalized MIMO (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input distributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and correlated channel matrices). The orthogonal/vector approximate message passing (OAMP/VAMP) receiver has been proved to be information-theoretically optimal in GMIMO, but it is limited to high-complexity linear minimum mean-square error (LMMSE). To solve this problem, a low-complexity memory approximate message passing (MAMP) receiver has recently been shown to be Bayes optimal but limited to uncoded systems. Therefore, how to design a low-complexity and information-theoretically optimal receiver for GMIMO is still an open issue. To address this issue, this paper proposes an information-theoretically optimal MAMP receiver and investigates its achievable rate analysis and optimal coding principle. Specifically, due to the long-memory linear detection, state evolution (SE) for MAMP is intricately multi-dimensional and cannot be used directly to analyze its achievable rate. To avoid this difficulty, a simplified single-input single-output (SISO) variational SE (VSE) for MAMP is developed by leveraging the SE fixed-point consistent property of MAMP and OAMP/VAMP. The achievable rate of MAMP is calculated using the VSE, and the optimal coding principle is established to maximize the achievable rate. On this basis, the information-theoretic optimality of MAMP is proved rigorously. Furthermore, the simplified SE analysis by fixed-point consistency is generalized to any two iterative detection algorithms with the identical SE fixed point. Numerical results show that the finite-length performances of MAMP with practical optimized low-density parity-check (LDPC) codes are 0.5 ~ 2.7 dB away from the associated constrained capacities. It is worth noting that MAMP can achieve the same performances as OAMP/VAMP with 4‰ of the time consumption for large-scale systems.

A signal scrambling based joint blind channel estimation, activity detection, and data decoding (SS-JCAD) scheme is proposed for coded massive random access. This signal scrambling technique imposes symbol-wise phase rotation to each user’s modulated data, and the scrambling pattern serves as a user-specific signature which is free from any bandwidth expansion or pilot signaling overhead. Building on this scrambling signature, we further propose a simple yet efficient receiver design, which integrates the blind channel state information (CSI) estimation module with the forward error correction (FEC) decoder. Specifically, according to the scrambling signature, a user-specific posterior probability density function of the CSI is derived, based on which both the CSI and activity of each user can be blindly detected using a low-complexity single-user maximum a posteriori estimation. Given the estimated CSI asa prioriinformation, a joint CSI (including user activity) estimation and data decoding algorithm is proposed, where the soft information is iteratively updated between the FEC decoder and the CSI estimation module to refine the detection reliability. Simulation shows that for massive random access systems with moderate code length and system load factor less than 1.5, the SS-JCAD scheme achieves almost the same bit error rate as the ideal case aided with perfect CSI, implying the SS-JCAD scheme as a near-optimal solution to the massive random access scenario.

This paper considers a generalized multiple-input multiple-output (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input dis-tributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and corre-lated channel matrices). Orthogonal/vector approximate message passing (OAMP/VAMP) has been proved to be information-theoretically optimal in GMIMO, but it is limited to high complexity. Meanwhile, low-complexity memory approximate message passing (MAMP) was shown to be Bayes optimal in GMIMO, but channel coding was ignored. Therefore, how to design a low-complexity and information-theoretic optimal receiver for GMIMO is still an open issue. In this paper, we propose an information-theoretic optimal MAMP receiver for coded GMIMO, whose achievable rate analysis and optimal coding principle are provided to demonstrate its information-theoretic optimality. Specifically, state evolution (SE) for MAMP is intricately multi-dimensional because of the nature of local memory detection. To this end, a fixed-point consistency lemma is proposed to derive the simplified variational SE (VSE) for MAMP, based on which the achievable rate of MAMP is calcu-lated, and the optimal coding principle is derived to maximize the achievable rate. Subsequently, we prove the information-theoretic optimality of MAMP. Numerical results show that the finite-length performances of MAMP with optimized LDPC codes are about 1.0 ~ 2.7 dB away from the associated constrained capacities. It is worth noting that MAMP can achieve the same performance as OAMP/VAMP with 4%o of the time consumption for large-scale systems.

The generalized linear system (GLS) has been widely used in wireless communications to evaluate the effect of nonlinear preprocessing on receiver performance. Generalized approximation message passing (AMP) is a state-of-the-art algorithm for the signal recovery of GLS, but it was limited to measurement matrices with independent and identically distributed (IID) elements. To relax this restriction, generalized orthogonal/vector AMP (GOAMP/GVAMP) for unitarily-invariant measurement matrices was established, which has been proven to be replica Bayes optimal in uncoded GLS. However, the information-theoretic limit of GOAMP/GVAMP is still an open challenge for arbitrary input distributions due to its complex state evolution (SE). To address this issue, in this paper, we provide the achievable rate analysis of GOAMP/GVAMP in GLS, establishing its information-theoretic limit (i.e., maximum achievable rate). Specifically, we transform the fully-unfolded state evolution (SE) of GOAMP/GVAMP into an equivalent single-input single-output variational SE (VSE). Using the VSE and the mutual information and minimum mean-square error (I-MMSE) lemma, the achievable rate of GOAMP/GVAMP is derived. Moreover, the optimal coding principle for maximizing the achievable rate is proposed, based on which a kind of low-density parity-check (LDPC) code is designed. Numerical results verify the achievable rate advantages of GOAMP/GVAMP over the conventional maximum ratio combining (MRC) receiver based on the linearized model and the BER performance gains of the optimized LDPC codes (0.8 ~ 2.8 dB) compared to the existing methods.

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates the sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as the unknown identity of active users and multiuser interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference-based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

The maximum achievable rate is derived for resistive random-access memory (ReRAM) channel with sneak-path interference. Based on the mutual information spectrum analysis, the maximum achievable rate of ReRAM channel with independent and identically distributed (i.i.d.) binary inputs is derived as an explicit function of channel parameters such as the distribution of cell selector failures and channel noise level. Due to the randomness of cell selector failures, the ReRAM channel demonstrates multi-status characteristic. For each status, it is shown that as the array size is large, the fraction of cells affected by sneak paths approaches a constant value. Therefore, the mutual information spectrum of the ReRAM channel is formulated as a mixture of multiple stationary channels. Maximum achievable rates of the ReRAM channel with different settings, such as single- and across-array codings, with and without data shaping, and optimal and treating-interference-as-noise (TIN) decodings, are compared. These results provide valuable insights on the code design for ReRAM.

Massive Machine-Type Communications (mMTC) scenario features a massive number of randomly activated user equipments (UEs). To efficiently support the random access for UEs in mMTC, a classifier diversity-combining based in-dependent component analysis (CDC-ICA) grant-free random access (GF-RA) scheme is proposed in this paper. Specifically, the base station (BS) employs multiple ICA classifiers for GF-RA detection. In each ICA classifier, an unsupervised learning technique, i.e. the independent component analysis (ICA) is employed to directly separate the source signals of active UEs from the received data signals. Furthermore, three strategies are proposed in the CDC-ICA scheme, i.e. independent ID encoding, noise-level estimation, and classifier diversity combining, to fully exploit the diversity provided by different ICA classifiers. Finally, simulation results show that the proposed CDC-ICA scheme outperforms existing ICA-based and compressed sensing (CS)-based GF-RA schemes in terms of the detection accuracy.

With the complication of future communication scenarios, most conventional signal processing technologies of multi-user multiple-input multiple-output (MU-MIMO) become unreliable, which are designed based on ideal assumptions, such as Gaussian signaling and independent identically distributed (IID) channel matrices. As a result, this paper considers a generalized MU-MIMO (GMU-MIMO) system with more general assumptions, i.e., arbitrarily fixed input distributions, and general unitarily-invariant channel matrices. However, there is still no accurate capacity analysis and capacity optimal transceiver with practical complexity for GMU-MIMO under the constraint of coding. To address these issues, inspired by the replica method, the constrained sum capacity of coded GMU-MIMO with fixed input distribution is calculated by using the celebrated mutual information and minimum mean-square error (MMSE) lemma and the MMSE optimality of orthogonal/vector approximate message passing (OAMP/VAMP). Then, a capacity optimal multi-user OAMP/VAMP receiver is proposed, whose achievable rate is proved to be equal to the constrained sum capacity. Moreover, a design principle of multi-user codes is presented for the multi-user OAMP/VAMP, based on which a kind of practical multi-user low-density parity-check (MU-LDPC) code is designed. Numerical results show that finite-length performances of the proposed MU-LDPC codes with multi-user OAMP/VAMP are about 2 dB away from the constrained sum capacity and outperform those of the existing state-of-art methods.

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input–multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

Massive machine-type communications (mMTC) is an important scenario to support Internet of Things (IoT) services. However, the massiveness of user equipments (UEs) poses new challenges for existing grant-free random access (GF-RA) schemes, such as pilot collisions and accumulation of failed UEs. To address this problem, we consider consecutive RA slots, and propose a cross-slot UE scheduling strategy for collision resolution in GF-RA systems. Specifically, different types of UEs are scheduled to select different sets of pilots via the feedback information. In this way, pilot collisions can be alleviated by dynamic UE scheduling. Then, we construct three deep neural networks (DNNs) for different collision-resolution tasks in UE scheduling, and these DNNs are trained to improve the scheduling efficiency. Furthermore, we adopt a matched training strategy for DNN training, which integrates the loss function of different DNNs to improve the output accuracy. Finally, a complete GF-RA scheme with DNN-aided UE scheduling (DNN-UESch-GFRA) is established. Simulation results are provided to verify the effectiveness of the matched training strategy, and show that the DNN-UESch-GFRA scheme can effectively resolve random access (RA) collisions and improve RA throughput.

In an adaptive or non-cooperative communication system, the blind identification of channel coding is an indispensable procedure for recovering the message from the intercepted coding data. Due to the wide applications of convolutional codes, the blind identification problem of convolutional codes has also received extensive studies. In this paper, we consider the blind identification problem of convolution encoder parameters in the general$k/n$rate case. To improve the blind identification accuracy of existing hard decision based solutions, a novel soft information based blind identification algorithm is designed in this paper. Specifically, a Soft Gaussian-Jordan Elimination Through Pivoting (Soft-GJETP) algorithm is firstly proposed to calculate the rank of the received data matrix. In contrast to the existing hard decision based GJETP algorithm, this Soft-GJETP algorithm formulates the soft information of the received data, then defines three reliability metrics and derives updating rules for this soft information. In this way, the diagonals of the received data matrix have fewer errors in Soft-GJETP algorithm. Furthermore, a soft decision strategy is proposed with a weight-dependent threshold. Employing this strategy, the dependent columns can be distinguished with higher accuracy. Finally, simulation results and comparisons are given to illustrate the performances of our proposed methods.

Conventional multi-user multiple-input multiple-output (MU-MIMO) mainly focused on Gaussian signaling, independent and identically distributed (IID) channels, and a limited number of users. It will be laborious to cope with the heterogeneous requirements in next-generation wireless communications, such as various transmission data, complicated communication scenarios, and unprecedented massive user access. Therefore, this paper studies a generalized MU-MIMO (GMU-MIMO) system with more generalized and practical constraints, i.e., practical channel coding, non-Gaussian signaling, right-unitarily-invariant channels (covering Rayleigh fading channel matrices, certain ill-conditioned and correlated channel matrices, etc.), and massive users and antennas. These generalized assumptions bring new challenges in theory and practice. For example, there is no accurate constrained capacity region analysis for GMU-MIMO. In addition, it is unclear how to achieve constrained-capacity-optimal performance with practical complexity. To address these challenges, a unified framework is proposed to derive the constrained capacity region of GMU-MIMO and design a constrained-capacity-optimal transceiver, which jointly considers encoding, modulation, detection, and decoding. Group asymmetry is developed to group users according to their rates, which makes a tradeoff between user rate allocation and implementation complexity. Specifically, the constrained capacity region of group-asymmetric GMU-MIMO is characterized by using the minimum mean-square error (MMSE) optimality of orthogonal/vector approximate message passing (OAMP/VAMP) and the relationship between mutual information and MMSE. Furthermore, a theoretically optimal multi-user OAMP/VAMP receiver and practical multi-user low-density parity-check (MU-LDPC) codes are proposed to achieve the constrained capacity region of group-asymmetric GMU-MIMO. Numerical results demonstrate that the proposed MU-LDPC coded GMU-MIMO systems achieve asymptotic performance within 0.2 dB from the theoretical sum capacity. Moreover, their finite-length performances are about 1~2 dB away from the associated sum capacity of GMU-MIMO.

Abstract A new random access scheme is proposed to solve the intra‐cell pilot collision for M2M communication in crowded asynchronous massive multiple‐input multiple‐output systems. The proposed scheme utilizes the proposed estimation method of signal parameters to estimate the effective timing offsets, and then active user equipments obtain their timing errors from the effective timing offsets for uplink message transmission. The mean squared error of the estimated effective timing offsets of user equipments and the uplink throughput are analysed. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive multiple‐input multiple‐output systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.

A novel grant-free random access scheme is proposed to support massive connectivity with low access delay and overhead for machine-to-machine communication in massive multiple-input-multiple-output systems. This scheme allows all active user equipments (UEs) to transmit their pilots and uplink messages via the same time-frequency resource and performs the joint active UEs detection and uplink message decoding without channel estimation in one shot by utilizing the proposed ensemble independent component analysis (EICA) decoding algorithm. We call the proposed scheme the EICA-based pilot random access (EICA-PA). We analyze the successful access probability, probability of missed detection, and uplink throughput of the EICA-PA scheme. Numerical results show that the EICA-PA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability and provides low-frame error rate at the same time.

With recent advances on the dense low-Earth orbit (LEO) constellation, the LEO satellite network has become one promising solution for providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli–Rician message passing with expectation–maximization (BR-MP-EM) algorithm is proposed for this terrestrial–satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation–maximization (EM) method is employed in the outer iterations to update the hyperparameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

Long Short-Term Memory (LSTM) network, a popular deep-learning model, is particularly useful for data with temporal correlation, such as texts, sequences, or time series data, thanks to its well-sought after recurrent network structures designed to capture temporal correlation. In this article, we propose to generalize LSTM to generic machine-learning tasks where data used for training do not have explicit temporal or sequential correlation. Our theme is to explore feature correlation in the original data and convert each instance into a synthetic sentence format by using a two-gram probabilistic language model. More specifically, for each instance represented in the original feature space, our conversion first seeks to horizontally align original features into a sequentially correlated feature vector, resembling to the letter coherence within a word. In addition, a vertical alignment is also carried out to create multiple time points and simulate word sequential order in a sentence (i.e.,word correlation). The two dimensional horizontal-and-vertical alignments not only ensure feature correlations are maximally utilized, but also preserve the original feature values in the new representation. As a result, LSTM model can be utilized to achieve good classification accuracy, even if the underlying data do not have temporal or sequential dependency. Experiments on 20 generic datasets show that applying LSTM to generic data can improve the classification accuracy, compared to conventional machine-learning methods. This research opens a new opportunity for LSTM deep learning to be broadly applied to generic machine-learning tasks.

This paper considers a low-complexity Gaussian message passing (GMP) Multi-User Detection (MUD) scheme for a coded massive multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (massive MIMO-NOMA), in which a base station with$N_{s}$antennas serves$N_{u}$sources simultaneously in the same frequency. Both$N_{u}$and$N_{s}$are large numbers, and we consider the overloaded cases with$N_{u}>N_{s}$. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. The GMP is attractive as its complexity order is only linearly dependent on the number of users, compared to the cubic complexity order of linear minimum mean square error (LMMSE) MUD. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. We prove that thevariancesof the GMP definitely converge to the mean square error (MSE) of the LMMSE multi-user detection. Second, themeansof the traditional GMP will fail to converge when$N_{u}/N_{s}< (\sqrt {2}-1)^{-2}\approx 5.83$. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any$N_{u}/N_{s}>1$, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, the numerical results are provided to verify the validity and accuracy of the theoretical results presented.

The problem of testing whether an incomplete tensor lies in a given tensor subspace, called tensor matched subspace detection, is significant when it is unavoidable to have missing entries. Compared with the matrix case, the tensor matched subspace detection problem is much more challenging due to the curse of dimensionality and the intertwinement between the sampling operator and the tensor product operation. In this paper, we investigate the subspace detection problem for the transform-based tensor models. Under this framework, tensor subspaces and the orthogonal projection onto a given subspace are defined, and the energies of a tensor outside the given subspace (also called residual energy in statistics) with tubal-sampling and elementwise-sampling are derived. We have proved that the residual energy of sampling signals is bounded with high probability. Based on the residual energy, the reliable detection is feasible.

Due to the massive number of devices in the M2M communication era, new challenges have been brought to the existing random-access (RA) mechanism, such as severe preamble collisions and resource block (RB) wastes. To address these problems, a novel sparse message passing (SMP) algorithm is proposed, based on a factor graph on which Bernoulli messages are updated. The SMP enables an accurate estimation on the activity of the devices and the identity of the preamble chosen by each active device. Aided by the estimation, the RB efficiency for the uplink data transmission can be improved, especially among the collided devices. In addition, an analytical tool is derived to analyze the iterative evolution and convergence of the SMP algorithm. Finally, numerical simulations are provided to verify the validity of our analytical results and the significant improvement of the proposed SMP on estimation error rate even when preamble collision occurs.

This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instance-feature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix, we use 0/1 optimization to reorder features such that the ones with strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification (Algorithm source code is available at http://github.com/hhmzwc/EDLT).

The common outage performance of an amplify‐and‐forward two‐hop two‐user channel is studied in the presence of multiple independent interferers at the destination. First, the exact integral form expression of the common outage probability is derived. Then, in order to decrease the computation complexity, a closed‐form approximation of the common outage probability is derived. Finally, the asymptotic analysis is performed based on the approximation expression. Numerical results demonstrate that the integral form of the common outage probability, the corresponding approximation and the asymptotic results match well with the Monte Carlo simulations.

MIMO-NOMA combines multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques to address heterogeneous challenges, such as massive connectivity, low latency, and high reliability in the 5G cellular communication system and beyond. In this paper, a coded MIMO-NOMA system with capacity-approaching performance and low implementation complexity is proposed. Specifically, the proposed MIMO receiver consists of a linear minimum mean-square error (LMMSE) multi-user detector and a bank of single-user message-passing decoders, which decompose the overall NOMA signal recovery into distributed low-complexity computations with iterative processing. An asymptotic extrinsic information transfer analysis is employed to model the overall performance, and a novel class of multi-user irregular repeat-accumulate channel codes that match with the LMMSE multi-user detector in the iterative decoding process are constructed for the system. As a result, the proposed coded MIMO-NOMA system achieves asymptotic performance within 0.2 dB from the theoretical capacity. Simulation results validate the reliability and robustness of the proposed system in practical settings that include different system loads, iteration numbers, code lengths, fast/block fading, and imperfect channel estimation.

In cloud radio access network (C-RAN), remote radio heads (RRHs) and users are uniformly distributed in a large area such that the channel matrix can be considered as sparse. Based on this phenomenon, RRHs only need to detect the relatively strong signals from nearby users and ignore the weak signals from far users, which is helpful to develop low-complexity detection algorithms without causing much performance loss. However, before detection, RRHs require to obtain the realtime user activity information by the dynamic grant procedure, which causes the enormous latency. To address this issue, in this paper, we consider a grant-free C-RAN system and propose a low- complexity Bernoulli-Gaussian message passing (BGMP) algorithm based on the sparsified channel, which jointly detects the user activity and signal. Since active users are assumed to transmit Gaussian signals at any time, the user activity can be regarded as a Bernoulli variable and the signals from all users obey a Bernoulli-Gaussian distribution. In the BGMP, the detection functions for signals are designed with respect to the Bernoulli-Gaussian variable. Numerical results demonstrate the robustness and effectivity of the BGMP. That is, for different sparsified channels, the BGMP can approach the mean-square error (MSE) of the genie-aided sparse minimum mean-square error (GA- SMMSE) which exactly knows the user activity information. Meanwhile, the fast convergence and strong recovery capability for user activity of the BGMP are also verified.

Low-cost message passing (MP) algorithm has been recognized as a promising technique for sparse vector recovery. However, the existing MP algorithms either focus on mean square error (MSE) of the value recovery while ignoring the sparsity requirement, or support error rate (SER) of the sparse support (non-zero position) recovery while ignoring its value. A novel low-complexity Bernoulli-Gaussian MP (BGMP) is proposed to perform the value recovery as well as the support recovery. Particularly, in the proposed BGMP, support-related Bernoulli messages and value- related Gaussian messages are jointly processed and assist each other. In addition, a strict lower bound is developed for the MSE of BGMP via the genie-aided minimum mean-square-error (GA-MMSE) method. The GA-MMSE lower bound is shown to be tight in high signal-to-noise ratio. Numerical results are provided to verify the advantage of BGMP in terms of final MSE, SER and convergence speed.

In this paper, we focus on the finite-length performance of spinal codes with a sliding window decoder over binary erasure channel. An expression of the error probability of spinal codes is derived. Particularly, we also derive an expression of the error probability of spinal codes with some known tail bits, which can improve the error-control performance. Moreover, easier-to-compute upper and lower bounds on the error probability are also provided. Simulation results show that the error-control performance can be improved by introducing known tail bits and the performance becomes better with the increase of the maximum window length. Finally, the derived bounds can well evaluate the error performance of spinal codes.

To resolve the pilot contamination problem in the massive multiple-input multiple-output (MIMO) systems, a pilot random access scheme which combines the strongest user collision resolution with timing advance information (SUCR-TA), is proposed. This scheme takes the propagation delay into account, and selects an appropriate TA information for each active pilot to reduce the number of contenders who will perform the SUCR algorithm. We also analyze the system throughput of the SUCRTA scheme, i.e. the number of MTC devices that can be allocated pilots successfully. Simulation results demonstrate that, compared with the SUCR algorithm, SUCR-TA scheme can significantly improve the system throughput.

Combination of multiple‐input‐multiple‐output and non‐orthogonal multiple access (MIMO–NOMA) is a promising multiple‐access technology, which can greatly improve spectral efficiency and reduce latency. Among major topics of MIMO–NOMA, an interesting one is how to construct suitable multiuser codes for MIMO–NOMA. In previous works, multiuser codes require to be redesigned when the number of users, transmit antennas, or receive antennas change. Therefore, the previous design methods are too complicated to be applied to MIMO–NOMA. To solve this problem, in this study, the authors first propose a simple multiuser detector (MUD) that detects the signal for each user by regarding the superimposed signal from the other users as interference. Then, based on extrinsic information transfer analysis for the MUD, they propose three criteria to simplify the code design, which copes with the changes of user number and antenna configuration. Moreover, when user number is large, each user requires a low‐rate code to overcome the severe multiuser interference. On the basis of the proposed criteria, they design a low‐rate repetition‐aided irregular repeat‐accumulate (Rep‐IRA) code for MIMO–NOMA with different numbers of users and antennas, which can achieve low complexity with the aid of repetition.

To resolve intra-cell pilot collision in crowded massive multiple-input multiple-output systems, a new pilot random access protocol, called strongest-user collision resolution combined graph-based pilots access (SUCR-GBPA), is proposed. This protocol allows all failed user equipments (UEs) to randomly select a pilot from the pilots that are not selected by any UE in the initial step. By exploiting the characteristic that the channel responses between UEs and the base station are invariant within the coherence time, a bipartite graph is established where active UEs and selected pilots are considered variable nodes and factor nodes, respectively. Based on this bipartite graph, the successive interference cancellation algorithm is employed to estimate the channel response of each UE. Finally, utilizing the and-or tree principle, we analyze the performance of the proposed SUCR-GBPA protocol, including the maximum number of the tolerable active UEs, the minimum number of the required pilots, uplink throughput, and the mean-square-error performance of the SUCR-GBPA channel estimation. Simulation results demonstrate that, compared with the SUCR protocol, the proposed SUCR-GBPA protocol significantly improves the uplink throughput and provides accurate estimation on the channel response at the same time.

This paper considers a low-complexity Gaussian Message Passing Iterative Detection (GMPID) algorithm for Multiple-Input Multiple-Output systems with Non-Orthogonal Multiple Access (MIMO-NOMA), in which a base station with $N_r$ antennas serves $N_u$ sources simultaneously. Both $N_u$ and $N_r$ are very large numbers and we consider the cases that $N_u>N_r$. The GMPID is based on a fully connected loopy graph, which is well understood to be not convergent in some cases. The large-scale property of the MIMO-NOMA is used to simplify the convergence analysis. Firstly, we prove that the variances of the GMPID definitely converge to that of Minimum Mean Square Error (MMSE) detection. Secondly, two sufficient conditions that the means of the GMPID converge to a higher MSE than that of the MMSE detection are proposed. However, the means of the GMPID may still not converge when $ N_u/N_rN_r$ with a faster convergence speed. Finally, numerical results are provided to verify the validity of the proposed theoretical results.

This paper considers a iterative Linear Minimum Mean Square Error (LMMSE) detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO) systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE detection greatly reduces the system computational complexity by departing the overall processing into many low-complexity distributed calculations. However, it is generally considered to be sub-optimal and achieves relatively poor performance. In this paper, we firstly present the matching conditions and area theorems for the iterative detection of the MIMO-NOMA systems. Based on the proposed matching conditions and area theorems, the achievable rate region of the iterative LMMSE detection is analysed. We prove that by properly design the iterative LMMSE detection, it can achieve (i) the optimal sum capacity of MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.

In this paper, we propose a rateless two-way spinal code. There exist two encoding processes in the proposed code, i.e., the forward encoding process and the backward encoding process. Rather than the original spinal code, where each message segment only has relationship with the coded symbols corresponding to itself and the later message segments, the information of each message segment of the proposed code is conveyed by the coded symbols corresponding to all the message segments. Based on this two-way coding strategy, we propose an iterative decoding algorithm. Different transmission schemes, including the symmetric transmission and the asymmetric transmission, are also discussed in this paper. Our analysis illustrates that the asymmetric transmission can be treated as a tradeoff between the performance and the decoding complexity. Simulation results show that the proposed code outperforms not only the original spinal code but also some strong channel codes, such as polar codes and raptor codes.

In this study, rate assignment for finite length non‐binary polar codes for arbitrary input discrete memoryless channels (AI‐DMC) which can be polarised into multiple levels is discussed. The closed form expression of the block error rate (BLER) of non‐binary polar codes when transmitted over an AI‐DMC is derived first. Then, an element‐wise exchange (EWEX) method is proposed to perform rate assignment to non‐binary polar codes aiming at minimising the BLER. It is also observed that the complexity of the EWEX method grows linearly with the size of the feasible set. The construction of finite length 4‐ary polar codes for a 4‐ary erasure channel which can be polarised into three levels is described in detail. Simulation results show that the codes constructed by the EWEX method have almost the same BLER performance as those constructed by the exhaustive searching method. Moreover, the estimated BLERs keep consistent with the simulation results.

In this paper, we propose and analyze an unequal error protection (UEP) rateless spinal code, which can provide UEP and unequal recovery time properties. The proposed UEP spinal codes achieve UEP by the permutation of different priority levels and the setup of different segment sizes for different priority levels. In addition, an unequal length transmission scheme to improve the transmission rate is proposed, where the number of transmitted symbols in each pass varies. Moreover, we analyze the finite-length performance of the proposed UEP spinal code, which enables us to provide the upper bound of average error probability for each priority level under maximum likelihood decoding. Based on this finite-length result, we discuss the design of UEP spinal code, which is flexible and efficient. Furthermore, the asymptotic performance analysis shows that the proposed UEP spinal code with a practical decoder can achieve the capacities of both binary symmetric channel and additive white Gaussian noise channel. Simulation results verify our analysis.

This paper considers a low-complexity Gaussian message passing iterative detection (GMPID) algorithm for a massive multiuser multiple-input multiple-output (MU-MIMO) system, in which a base station with$M$antennas serves$K$Gaussian sources simultaneously. Both$K$and$M$are very large numbers, and we consider the cases that$K<M$. The GMPID is a message passing algorithm operating on a fully connected loopy graph, which is well understood to be non-convergent in some cases. As it is hard to analyze the GMPID directly, the large-scale property of the massive MU-MIMO is used to simplify the analysis. First, we prove that the variances of the GMPID definitely converge to the mean square error of minimum mean square error (mmse) detection. Second, we derive two sufficient conditions that make the means of the GMPID converge to those of the mmse detection. However, the means of GMPID may not converge when$ K/M\geq (\sqrt {2}-1)^{2}$. Therefore, a modified GMPID called scale-and-add GMPID, which converges to the mmse detection in mean and variance for any$K

In this paper, we propose a rateless superposition spinal coding scheme for the half-duplex relay channel, which is an optimal realization of the information theoretic coding scheme for decode-and-forward (DF) relaying. The proposed coding scheme has a simple coding structure and can be implemented flexibly. More importantly, the encoders and decoders can maintain unchanged for varying channel conditions. We also contribute to the optimization of the proposed coding scheme, which can provide an excellent rate performance. Since only two parameters are considered in the optimization, the optimization can be realized with low complexity. Furthermore, we prove that the proposed coding scheme can achieve the theoretic limit of half-duplex DF relaying. Simulation results show that the average transmission rates of the proposed coding scheme are very close to the theoretic limits for both Gaussian relay channel and fading relay channel.

Rate regions of a multi‐way relay channel with direct links (MWRC‐DLs), where K users exchange their messages via a relay terminal and all users can overhear each other directly, are studied in this study. Under the assumption that a restricted encoder is employed at each user, the cut‐set outer bound on the capacity region is derived first. Then, achievable rate regions of the MWRC‐DLs with decode‐and‐forward (DF) and compress‐and‐forward (CF) strategies are characterised. Meanwhile, the explicit expressions of the outer bound and the achievable rate regions for the Gaussian MWRC‐DLs are also derived. It is shown that the rate regions of the DF and CF strategies for the two‐way relay channel and the multiple‐access relay channel can be obtained from those of the MWRC‐DLs. To give more insights on the two strategies of the MWRC‐DLs system, the common rates of a symmetric Gaussian network are analysed. It is shown that the CF strategy achieves common rates within 1/2( K − 1) bits of the capacity when the relay's power is at least ( K − 1) times as large as the user power. Numerical examples are also provided to verify the theoretical analysis.

In this paper, we propose interference alignment (IA) schemes for downlink multiple-input-multiple-output heterogeneous networks (HetNets) with partial connectivity, which is induced by the path loss and the low transmission power of small cells. Specifically, we consider two partially connected scenarios of HetNets. In the first scenario, we focus on the partial connectivity among small cells, whereas in the second scenario, we further consider the partial connectivity between the macrocell and small cells. For the first scenario, we first propose a two-stage IA scheme by exploiting the heterogeneity and partial connectivity of HetNets. Then, the influence of the number of served macro users on system degrees of freedom (DoFs) is investigated. In particular, we derive the condition under which serving one macro user achieves more DoFs than serving multiple macro users and design an algorithm to find the optimal number of served macro users to maximize the system DoFs. Afterward, we study the second scenario and extend the two-stage IA to this scenario. The simulation results show that the proposed IA schemes can significantly improve the system sum rate. Moreover, by considering the partial connectivity between the macro cell and small cells, the system performance can be further improved.

There have been five constructions (Cases I to V) of 64-QAM Golay complementary sequences (GCSs), of which the Cases IV and V constructions were identified by Chang in 2010. The Generalized Cases I-III constructions for 4q-QAM (q ≥ 1) GCSs were additionally proposed by Li. In this paper, the Generalized Case IV and Generalized Case V constructions for 4q-QAM (q > =3) GCSs are proposed using selected Gaussian integer pairs, each of which contains two distinct Gaussian integers with identical magnitude and which are not conjugate with each other.

A novel network coding based coordinated multi- point (NC-CoMP) process, which allows one of the cooperative base stations (BSs) to serve one more non-cell-edge UE simultaneously in its own cell, is proposed in this paper. To implement the NC-CoMP transmission, an alignment combined precoding(ACP) algorithm is designed to eliminate the inter-user interference and simultaneously to extract useful signal from the network coded signals. Two algorithms are presented to optimize the precoding vectors with the aim at maximizing the receive signal-to-noise rate(SNR) of the cell-edge users. Compared with the conventional two UE multi-user coherent joint processing CoMP (CJP-CoMP) scheme, the proposed NC-CoMP scheme can increase the date throughput significantly without requiring any extra resources. Simulations also illustrate that the bit error rate (BER) performance of the cell-edge users can be improved significantly when the NC-CoMP scheme is employed.

Based on the relaying scheme which permits the relay node to jointly encode the data information from two source nodes, a new network low density parity check (NLDPC) code is designed for the Rayleigh fading channel. Since the log-likelihood ratio (LLR) of the signal received from the fading channel is not a Gaussian random variable, the Gaussian Approximation algorithm cannot be employed directly in the density evolution. In this paper, a new hybrid bilayer Gaussian Approximation algorithm is derived to predict the performance of NLDPC codes over Rayleigh fading channels, based on which an optimization algorithm is developed to search for the degree distribution of the NLDPC code. Simulations show that, in a network with two sources, one relay and one destination, the system with the designed NLDPC code holds a performance gap of less than 0.6dB from the threshold.

In this paper, we consider user selection algorithms for Multi-user MIMO systems with perfect or partial channel state information at the transmitter (CSIT). A novel user selection algorithm based on chordal distance is proposed. The basic principle is to serve the user subset in which one is approximately orthogonal to the others. Since the chordal distance relies only on the channel direction information, the proposed algorithm has a low computational complexity, and can be extended straightly to the limited feedback system. Analysis and simulations verify the effectiveness of the proposed method.

This paper is concerned with the construction of a class of nonbinary irregular repeat accumulate (IRA) codes. Since they are defined on the finite field GF(q) (q>2), we will refer to the constructed codes as q-ary IRA (QIRA) codes. While preserving the excellent error correcting capability of q-ary LDPC codes, QIRA codes can be efficiently encoded like conventional binary IRA codes. By adopting the progressive edge growth (PEG) algorithm to construct the parity check matrices, we can achieve the increased girth of their factor graphs and improved decoding performance. Simulation results show that, using the sum-product algorithm on GF(q), QIRA codes outperform binary LDPC codes and turbo codes in terms of bit error ratio and frame error ratio on AWGN channels. Especially, they could achieve excellent error performance when combined with high order modulations. Feasibility study indicates, with the use of the extended min-sum (EMS) decoding algorithm, QIRA codes are competitive candidates for practical applications.

To reduce the detection complexity of the space-time bit-interleaved coded modulation(ST-BICM) scheme, a turbo type iterative receiver structure consisting of a new group Gaussian approximation(GGA) algorithm and an a posteriori probability(APP) decoder is presented. When calculating the log-likelihood ratio(LLR) of the i-th transmitted symbol, the GGA algorithm first divide the N transmitted signals into two groups, a detecting group consisting of G signals with the i-th transmitted signal included, and an interfering group consisting of the other N-G signals. Taking the superposition of the N-G signals as a Gaussian random variable, the GGA algorithm implements the maximum a posteriori(MAP) detection to obtain the LLR of the signal transmitted from antenna i. At high SNR values or if the receiver has more than two replicas of each transmitted signal, the GGA detection exhibits a good performance as well as low decoding complexity.

Using high rate recursive systematic convolutional code (RSCC) as the basic element and the trace criteria as the design principle, a new kind of high rate recursive space-time trellis code (HR-RSTTC) is designed for serial concatenation. By making the number of RSCCs and the coding rate of each RSCC be variable, our designed HR-RSTTC can adjust the data rate according to the transmit antenna number and the modulation scheme. It is verified that, in independent fading channels, the minimum diversity gains of the serially concatenated space-time trellis code (SCSTTC) constructed with HR-RSTTC is 2M(M is the number of receive antennas). In comparison with the traditional SCSTTC, the new SCSTTC can achieve the same data rate with lower complexity and no performance degradation. With the same transmit antenna number and modulation scheme, the new SCSTTC have higher data rate than the traditional SCSTTC

A new class of recursive convolutional space-time codes (ReC-STC) with an arbitrary number of transmit antennas is designed by adopting several parallel two-state recursive systematic convolutional codes (RSCs). An intercross linear mapping rule that distributes the output bits of an RSC to the different positions of different transmitted signals is also described. The proposed ReC-STC can not only increase the data rate with the number of transmit antennas, but also performs well when it is used in a serially concatenated space-time code (SCSTC). The convergence analysis shows that ReC-STC based SCSTC has a lower decoding threshold than the available SCSTC.