This paper is concerned with sparse code multiple access (SCMA) with protograph-based low-density parity-check (P-LDPC) codes over Rayleigh fading channels. To be specific, we first propose a two-step design method consisting of both low-density factor graph (LDFG) and energy-based constellation superposition (ECS) principles to construct a new type of non-uniform codebooks (CBs), which have superior robustness against inter-user interference. We also develop a new segment-wise interleaved transmission (SWIT) scheme, which features a systematic optimization among repetition coding, resource mapping, and cross-slot data coupling, to further improve the performance of P-LDPC-coded SCMA. Additionally, we conceive a decoding-threshold-guided multi-objective optimization (DT-MOO) method to construct protograph-based enhanced multi-user codes (EMUCs), which exhibit excellent error-rate performance under both turbo and non-turbo decoding compared to traditional single-user and existing multi-user codes. Theoretical and simulated results validate the superiorities of proposed non-uniform SCMA scheme with SWIT and EMUCs compared to state-of-the-art benchmarks. Owing to the above advantages, the proposed designs are competent to provide massive connectivity and energy-efficient transmission for simultaneous wireless information and power transfer (SWIPT) applications in 6G networks.

As wireless communication applications evolve from traditional multipath environments to high-mobility scenarios like unmanned aerial vehicles, multiplexing techniques have advanced accordingly. Traditional single-carrier frequency-domain equalization (SC-FDE) and orthogonal frequency-division multiplexing (OFDM) have given way to emerging orthogonal timefrequency space (OTFS) and affine frequency-division multiplexing (AFDM). These approaches exploit specific channel structures—e.g., Toeplitz-structured multipath channel matrix for OFDM and SC-FDE or doubly selective channels for OTFS and AFDM—to diagonalize or sparsify the effective channel, thereby enabling low-complexity detection. However, their reliance on these structures significantly limits their robustness in dynamic, real-world environments. To address these challenges, this paper studies a random multiplexing technique that is decoupled from the physical channels, thereby enabling its application to arbitrary norm-bounded and spectrally convergent channel matrices. Random multiplexing achieves statistical fading-channel ergodicity for transmitted signals by constructing an equivalent input-isotropic channel matrix in the random transform domain. It guarantees the asymptotic replica MAP bit-error rate (BER) optimality of AMP-type detectors for linear systems with arbitrary norm-bounded, spectrally convergent channel matrices and signaling configurations, under the unique fixed point assumption. A low-complexity cross-domain memory AMP (CD-MAMP) detector is considered for random multiplexing systems, leveraging the sparsity of the time-domain channel and the input isotropy of the equivalent channel. Optimal power allocations are derived to minimize the replica MAP BER and maximize the replica constrained capacity of random multiplexing systems, respectively. The optimal coding principle and replica constrained-capacity optimality of CD-MAMP detector are investigated for random multiplexing systems. Additionally, the versatility of random multiplexing in diverse wireless applications is explored. Numerical results are presented to validate the theoretical findings.

Multimodal recommender systems try to integrate multimedia data (images, texts, etc.) with user-item historical records to better model user preference. However, most previous methods largely ignored the underlying fine-grained attribute features of items, which makes it difficult to fully explore users' nuanced attention across individual and combined attributes, resulting in low recommendation performance. To address these issues, this paper proposes a novel and effective self-harmonized representation learning network for multimodal recommendation, named LETTER. LETTER has the ability to effectively optimize the user and item representations for multimodal recommendation. Specifically, we design a factorized attribute interaction module that captures diverse combinations of item latent attributes using a bilinear pooling strategy. Then a dual graph convolution module is established to learn the modality-specific representations from user-item interactive and item semantic relations. Finally, we design a preference self-harmonization module that adaptively identifies the salient influencing factors of user preference, thus refining user and item representations to improve recommendation accuracy. We conduct extensive experiments on three real-world datasets, demonstrating that LETTER outperforms state-of-the-art multimodal recommendation methods.

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.

This paper introduces a random modulation technique that is decoupled from the channel matrix, allowing it to be applied to arbitrary norm-bounded and spectrally convergent channel matrices. The proposed random modulation constructs an equivalent dense and random channel matrix, ensuring that the signals undergo sufficient statistical channel fading. It also guarantees the asymptotic replica maximum a posteriori (MAP) bit-error rate (BER) optimality of approximate message passing (AMP)-type detectors for linear systems with arbitrary norm-bounded and spectrally convergent channel matrices when their state evolution has a unique fixed point. Then, a lowcomplexity cross-domain memory approximate message passing (CD-MAMP) detector is proposed for random modulation, leveraging the sparsity of the time-domain channel and the randomness of the random transform-domain channel. Furthermore, the optimal power allocation schemes are derived to minimize the replica MAP BER and maximize the replica constrained capacity of random-modulated linear systems, assuming the availability of channel state information (CSI) at the transceiver. Numerical results show that the proposed random modulation can achieve BER and block-error rate (BLER) performance gains of up to$2 \sim 3 \mathbf{d B}$compared to existing OFDM/OTFS/AFDM with 5G-NR LDPC codes, under both average and optimized power allocation.

Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in this area, a comprehensive exploration of the intricate relationship between AIGC and communication networks remains relatively limited. To address this issue, this article conducts an exhaustive survey from dual standpoints: first, it scrutinizes the integration of LLMs and AIGC technologies within the domain of communication networks and second, it investigates how the communication networks can further bolster the capabilities of LLMs and AIGC. Additionally, this research explores the promising applications along with the challenges encountered during the incorporation of these AI technologies into communication networks. Through these detailed analyses, our work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks.

Iterative decoding algorithms based on the alternating direction method of multipliers (ADMM) decoding of low density parity check (LDPC) codes has emerged as an alternating decoding method and bringed a boom of research on drawing upon mathematical optimization to LDPC decoding. Improving error-correcting performance is a key issue to enhance the superiority of ADMM decoding. In this letter, we investigate an efficient ADMM-based iterative decoder for binary LDPC codes. First, we build an mathematical programming equivalence of the maximum likelihood (ML) decoding problem by transforming parity-check constraints to multiple equivalent linear constraints and eliminating check-polytope projection (CPP). Then, an iterative algorithm based on ADMM technique is developed to solve this free-of-CPP (FCPP) equivalence and each ADMM update can be computed efficiently. Moreover, the proposed ADMM-FCPP decoding algorithm is analyzed to display a linear complexity to the length of the LDPC code at each iteration. Finally, simulation results demonstrate the superiority of the proposed decoder in error-correcting performance compared with the state-of-the-art ADMM-based decoders.

With the rapid popularity of vision-driven communication applications, object detection has become one of the fundamental techniques for performing practical vision tasks. In traditional communication systems, images are compressed for transmission, reconstructed at the receiver, and then processed by existing object detection algorithms. However, transmitting large amounts of images consumes significant storage and communication resources. To address this challenge, a semantic communication-based image reconstruction scheme has been proposed for object detection, which transmits only the semantic information relevant to image reconstruction. However, this method is prone to losing key information, such as object position and texture details, leading to degraded object detection performance. Additionally, it is sensitive to environmental factors such as weather and lighting, resulting in poor adaptability across multiple scenarios. To address these issues, we propose a multi-scale semantic communication framework for object detection that transmits only multi-scale semantic features relevant to the task and employs decoupling at the receiver to separate positional and classification information of target objects without requiring image reconstruction. To improve adaptability across multiple scenarios, we introduce a cross-domain object detection technique that ensures reliable object detection in new scenarios by optimizing the framework’s multi-scale semantic encoder through domain adversarial learning. Numerical results demonstrate that the proposed framework achieves mean average precision improvements of$15.4\% \sim 38.5\%$over the traditional communication framework within low to medium signal-to-noise ratio regions in additive white Gaussian noise and Rayleigh fading channels.

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 (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.

Multilabel remote sensing image classification can provide comprehensive object-level semantic descriptions of remote sensing images. However, most existing methods cannot fully mine the fine-grained features of images and labels, resulting in low classification accuracy. To address this issue, we propose a novel multitask framework for multilabel remote sensing image classification. The framework establishes the class-specific feature extraction as a binary classification auxiliary task to assist the main multilabel classification task, which can improve the model’s local and global feature extraction ability. Meanwhile, the framework updates the label correlation graph using the graph transformer layer to accurately identify label node pairs with potential correlation, which effectively mines the correlation of multiple labels to generate more accurate label co-occurrence embedding for image label prediction. Experimental results on UCM, AID, and DFC15 multilabel datasets show that the proposed method outperforms existing state-of-the-art methods.

Multimodal recommendation is an emerging task with the goal of improving the effectiveness of the recommendation system by utilizing multimodal data (images, texts, etc.). Most previous methods have struggled with the ability to mine item semantic relationships while guaranteeing accurate modeling of user modality preferences, resulting in low recommendation accuracy. To address this issue, this paper proposes a novel and effective Self-suPervised duAl preference enhanCing nEtwork for multimodal recommendation, named SPACE, which further mines user preferences towards historical interactions and multimodal features of items to obtain more precise user and item representation. Specifically, we design an interaction preference enhancing module to learn both interactive and latent semantic relationships between users and items. Then, a modality preference enhancing module is established by introducing self-supervised learning (SSL), which aims to strengthen the role of dominant modality-specific representation of items. Finally, the enhanced interaction and modality representations are fused, and the recommendation performance is largely improved by utilizing dual joint prediction. Extensive experiments are conducted on three real-world datasets, and the simulation results demonstrate that the proposed SPACE model outperforms the state-of-the-art multimodal recommendation methods.

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.

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.

Image-text retrieval (ITR) is a challenging task in the field of multimodal information processing due to the semantic gap between different modalities. In recent years, researchers have made great progress in exploring the accurate alignment between image and text. However, existing works mainly focus on the fine-grained alignment between image regions and sentence fragments, which ignores the guiding significance of context background information. Actually, integrating the local fine-grained information and global context background information can provide more semantic clues for retrieval. In this paper, we propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment. To justify the proposed model, we perform extensive experiments on MS-COCO and Flickr30 K datasets. Experimental results show that the proposed HGAN outperforms the state-of-the-art methods on both datasets, which demonstrates the effectiveness and superiority of our model.

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.

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.

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.

A very low-complexity on-off division multiple access (ODMA) scheme is proposed for K-user non-orthogonal multiple access (NOMA) systems. At the transmission side, each user employs the same length-m channel code whose coded bits, after modulation, are sent in a random time-hopping manner. Specifically, m coded bits are randomly scheduled and sent using n time slots with n≫m, i.e., only m slots are used for signal transmission and the other n- m slots are idle. The slot selection, referred to as an on-off pattern, is unique to each user, and it is the only means of user separation. Consequently, at each time slot only a very few users (i.e., 2 or 3) may simultaneously access the channel, leading to a super-sparse access system. Due to the sparse access property, a very low-complexity iterative multi-user decoding method can be implemented on an almost tree-like factor graph. Compared with existing iteratively decodable code division multiple access (CDMA) schemes, such as sparse-CDMA and interleave division multiple access (IDMA), ODMA does not rely on repetition (spreading) or user interleaving. In fact, we show that in using extrinsic information transfer (EXIT) analysis and simulation, idling is more effective than repetition in terms of enhancing the multi-user iterative decoding performance. By replacing repetition with idling, a remarkable multi-user decoding performance gain is achieved and, at the same time, the decoding complexity is significantly reduced.

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.

Though the concept of non-orthogonal multiple access (NOMA) was proposed several years ago, the performance of uplink NOMA has only been verified in theory, but not in practice. This paper presents an over-the-air implementation of a uplink NOMA system, while providing solutions to most common practical problems, i.e., carrier frequency offset (CFO) synchronization, time synchronization, and channel estimation. The implemented CFO synchronization method adopts the primary synchronization signal (PSS) of LTE. Also, we design a novel preamble for each uplink user, and it is appended to every frame before it is transmitted through the air. This preamble will be used for time synchronization and channel estimation at the BS. Also, a low-complexity, iterative linear minimum mean squared error (LMMSE) detector has been implemented for multi- user decoding. The paper also validates the proposed architecture numerically, as well as experimentally.

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.

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.