Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, deep learning (DL) based channel extrapolators have become promising candidates for modeling complex frequency-domain dependency. Nevertheless, current DL extrapolators fail to operate in unseen environments under distribution shift, which poses challenges for large-scale deployment. In this paper, environment generalizable learning for channel extrapolation is achieved by realizing distribution alignment from a physics perspective. Firstly, the distribution shift of wireless channels is rigorously analyzed, which comprises the distribution shift of multipath structure and single-path response. Secondly, a physics-based progressive distribution alignment strategy is proposed to address the distribution shift, which includes successive path-oriented design and path alignment. Path-oriented DL extrapolator decomposes multipath channel extrapolation into parallel extrapolations of the extracted paths, which can mitigate the distribution shift of multipath structure. Path alignment is proposed to address the distribution shift of single-path response in path-oriented DL extrapolators, which eventually enables generalizable learning for channel extrapolation. In the simulation, distinct wireless environments are generated using the precise ray-tracing tool. Based on extensive evaluations, the proposed path-oriented DL extrapolator with path alignment can reduce extrapolation error by more than 6 dB in unseen environments compared to the state-of-the-arts.

Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the generalizability of current deep learning-based CSI feedback algorithms cannot be guaranteed in unseen environments, which induces a high deployment cost. In this paper, the generalizability of deep learning-based CSI feedback is promoted with physics interpretation. Firstly, the distribution shift of the cluster-based channel is modeled, which comprises the multi-cluster structure and single-cluster response. Secondly, the physics-based distribution alignment is proposed to effectively address the distribution shift of the cluster-based channel, which comprises multi-cluster decoupling and fine-grained alignment. Thirdly, the efficiency and robustness of physics-based distribution alignment are enhanced. Explicitly, an efficient multi-cluster decoupling algorithm is proposed based on the Eckart–Young-Mirsky (EYM) theorem to support real-time CSI feedback. Meanwhile, a hybrid criterion to estimate the number of decoupled clusters is designed, which enhances the robust-ness against channel estimation error. Fourthly, environment-generalizable neural network for CSI feedback (EG-CsiNet) is proposed as a novel learning framework with physics-based distribution alignment. Based on extensive simulations and sim-to-real experiments in various conditions, the proposed EG-CsiNet can robustly reduce the generalization error by more than 3 dB compared to the state-of-the-arts.

Despite the rapid advancements in artificial intelligence, the preparation of large-scale labeled training data continues to pose a significant challenge, particularly within the manufacturing sector where annotations are frequently inadequate. This study introduces an entropy-based method for annotation-free image regularity assessment, with a specific focus on wire and cable distribution. Our approach encompasses three key components: (i) an analysis of spatial entropy pertaining to linear objects, (ii) a quantification of positional confusion present in images, and (iii) the provision of a standardized metric applicable to wiring operations. The proposed method provides a clear physical interpretation of entropy for annotation-free assessments while generating quantifiable and ordered entropy values that enable automated labeling and fully automated regularity analysis. Experimental results demonstrate that our approach surpasses human annotation in accuracy, thereby offering an objective and scalable solution.

In scenarios where the number of users far exceeds the number of base station antennas, it becomes infeasible to serve all users simultaneously. We observe that scheduling more users at first steadily improves system performance, but this improvement eventually reaches saturation. Moreover, scheduling excessive users per time slot results in diminishing gains per user and a significant increase in computational complexity. Consequently, only a subset of users can be selected for service in each time slot. Given that the fairness scheduling and precoding problems are coupled, we employ learning-based methods to rapidly schedule users, reducing the complexity of subsequent joint optimization problems. To facilitate effective backpropagation despite discrete scheduling actions, we design the network's output layer accordingly and introduce a layer-wise pruning mechanism to enhance learning accuracy and speed. By leveraging the interference relationships, we partition the base station into clusters, thereby transforming the centralized joint optimization problem into multiple sub-problems, which can be tackled in a distributed manner.

Efficient power allocation and interference management are critical challenges in dynamic wireless communication systems. To address these challenges, graph neural networks (GNNs) have attracted significant attention, while knowledge graph further enhance this capability by representing structured interactions among entities. This article proposes the Power-focused Knowledge Graph Convolutional Network (PKGCN), a novel framework utilizing knowledge graph driven learning to model and optimize power allocation strategies. By integrating wireless-specific features such as channel conditions and interference metrics, PKGCN effectively captures the complex interactions and dependencies among network nodes. This model employs a message aggregation layer to extract local and global interactions and a power prediction layer to optimize resource allocation. Comprehensive evaluations reveal that PKGCN de-livers higher average user rates, lower interference levels, and greater robustness.

Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this article, we developed a distributed queue model for multitask deep neural network inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided proximal policy optimization algorithm, named LyaPPO. Through numerical results, we show that our LyaPPO algorithm outperforms the baseline algorithms. Specifically, under different maximum local computational capacities, the proposed algorithm decreases 24.29%–56.62% energy compared to the suboptimal baselines.

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.

For frequency division duplex (FDD) systems, downlink channel state information (CSI) feedback is essential. Deep learning-based auto-encoder (AE) structures have shown promise in reducing feedback overhead. However, designing a super-large AE network to handle the CSI of all scenarios is not practical. A more practical approach is to divide the CSI dataset by region/scenario and use multiple simple AE networks. However, this method requires high memory capacity, making it unsuitable for low-end user equipment (UE). In this paper, we propose a new UE-friendly framework based on multi-tasking mode. Our framework, called single-encoder-to-multiple-decoders (S-to-M), uses multi-task-learning to design multiple independent AEs into a joint architecture with a shared encoder that corresponds to multiple task-specific decoders. We also integrate GateNet as a classifier to enable the base station to autonomously select the right task-specific decoder for the subregion. Our experiments on a simulated multi-scenario CSI dataset show that our proposed S-to-M framework outperforms other benchmark modes by significantly reducing model complexity and UE memory consumption.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS, and the user equipment (UE), is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.

Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.

5G network is anticipated to meet the challenging requirements of mobile traffic in the 2020's, which are characterized by super high data rate, low latency, high mobility, high energy efficiency, and high traffic density. This paper provides an overview of China Mobile's 5G vision and potential solutions. Targeting a paradigm shift to user-centric network operation from the traditional cell-centric operation, 5G radio access network (RAN) design considerations are presented, including RAN restructure, Turbo charged edge, core network (CN) and RAN function repartition, and network slice as a service. Adaptive multiple connections in the user-centric operation is further investigated, where the decoupled downlink and uplink, decoupled control and data, and adaptive multiple connections provide sufficient means to achieve a 5G network with “no more cells.” Software-defined air interface (SDAI) is presented under a unified framework, in which the frame structure, waveform, multiple access, duplex mode, and antenna configuration can be adaptively configured. New paradigm of 5G network featuring user-centric network (UCN) and SDAI is needed to meet the diverse yet extremely stringent requirements across the broad scope of 5G scenarios.

Millimeter wave (mmWave) massive MIMO can achieve orders of magnitude increase in spectral and energy efficiency, and it usually exploits the hybrid analog and digital precoding to overcome the serious signal attenuation induced by mmWave frequencies. However, most of hybrid precoding schemes focus on the full-array structure, which involves a high complexity. In this paper, we propose a near-optimal iterative hybrid precoding scheme based on the more realistic subarray structure with low complexity. We first decompose the complicated capacity optimization problem into a series of ones easier to be handled by considering each antenna array one by one. Then we optimize the achievable capacity of each antenna array from the first one to the last one by utilizing the idea of successive interference cancelation (SIC), which is realized in an iterative procedure that is easy to be parallelized. It is shown that the proposed hybrid precoding scheme can achieve better performance than other recently proposed hybrid precoding schemes, while it also enjoys an acceptable computational complexity.

Linear precoding techniques, such as zero forcing precoding, can achieve the near-optimal capacity due to the favorable channel propagation in large-scale MIMO systems, but involve complicated matrix inversion of large size. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. We also prove that the proposed GSbased precoding scheme has a faster convergence rate than the recently proposed Neumann-based precoding scheme. Simulation results demonstrate that the proposed scheme can achieve the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations.

Non-orthogonal multiple access (NOMA) is expected to be a promising multiple access techniques for 5G networks due to its superior spectral efficiency (SE). In this paper, we study the energy efficiency (EE) optimization for the fading multiple-input multiple-output (MIMO) NOMA systems with statistical channel state information (CSI) at the transmitter. The EE optimization problem is formulated to maximize the system EE (defined by ergodic capacity under unit power consumption) under the total transmit power constraint and the minimum rate constraint of the weak user. We propose the near optimal power allocation schemes and also the suboptimal closed-form solutions. Numerical results show that the proposed NOMA schemes significantly outperform the traditional orthogonal multiple access scheme with open loop MIMO transmission in terms of both SE and EE.

Hybrid beamforming has drawn attention with the increasing concern on high frequency band communication and the appearance of flexible structures of base stations. This paper considers the practical transmitter structure that each antenna is only connected to a unique RF chain and optimizes the analog and digital beamforming matrices to maximize the achievable rate with transmit power constraint. Following alternating optimization principle, closed-form relationships between analog and digital precoders are obtained when both amplitude and phase or only phase can be adjusted to form analog beams. Simulation results demonstrate the advantage of our proposed methods over the existing beam steering method in terms of achievable rate with different scale antenna array. This reveals that the method can be applied to both low and high frequency band communications. When increasing the number of propagation paths, contrast to the traditional beam steering method, the performance of proposed methods becomes better while the complexity almost keeps at an acceptable constant level.

Non-orthogonal multiple access (NOMA) is expected to be a promising technique for future wireless networks due to its superior spectral efficiency. In this paper, the sum rate optimization problem for multiple-input multiple-output (MIMO) NOMA systems is studied with the total transmit power constraint and the minimum rate constraint of weak user. We first derive a channel state information (CSI) condition in which MIMO NOMA systems can achieve full rate transmission, i.e. the transmission rate of the weak user equals to the channel capacity of weak user. Based on the CSI condition, we propose an optimal power allocation scheme for MIMO NOMA systems, which can achieve the capacity region of MIMO Broadcast channel as dirty paper coding. A low complexity suboptimal scheme is proposed as well for all CSI channel conditions. Numerical results show that the proposed NOMA schemes significantly outperform the traditional time division based single user MIMO scheme and the multi-user MIMO scheme.

Mobile traffic is projected to increase 1000 times from 2010 to 2020. This poses significant challenges on the 5thgeneration (5G) wireless communication system design, including network structure, air interface, key transmission schemes, multiple access, and duplexing schemes. In this paper, full duplex (FD) is discussed, aiming to highlight some insights on various issues including deployment scenarios, frame structures, reference signals (RS), interference mitigation, transceiver structures/calibration, and extension of time division duplex (TDD)/frequency division duplex (FDD) to full duplex. It is anticipated that with future standardization and deployment of FD systems, TDD and FDD will be harmoniously integrated, supporting all the existing half duplex mobile phones efficiently, and leading to a much enhanced 5G system performance.

This paper investigates optimal transmission of space-time block codes (STBCs) in distributed multiple-input multiple-output (D-MIMO) Rayleigh fading channels. The optimal diversity performance is achieved through transmit power allocation implemented at the receiver based on transmit and receive correlations to minimize the average symbol error rate (SER). Evaluation of SER performance of uncoded STBCs over a generalized distributed antenna (DA) topology is first presented, with exact analytical SER expressions derived for MQAM and MPSK symbols. SER upper bounds are also derived, based on which two criteria for complexity reduced antenna subset selection with sub-optimal power allocation are further proposed, whose performance approaches optimal over correlated D-MIMO channels. Moreover, a novel simplified but close SER approximation scheme is devised to significantly facilitate optimal SER calculation. We continue to thoroughly analyze how the optimal diversity is affected by large scale fading, targeted data rate, antenna correlations and transmit power. Finally, we develop a surprisingly close and useful analogy between open loop STBCs in co-located MIMO and optimal STBCs in D-MIMO with minimum feedback (i.e., n bits for n DAs in Criterion 2 with power allocation scheme 2 which equally allocates power to the selected DAs). Extensive simulation results have been presented to demonstrate the effectiveness of our analysis.

This letter investigates optimal transmission of orthogonal space-time block codes (OSTBCs) over distributed antennas (DAs) in non-ergodic flat Rayleigh fading channels with transmit antenna correlations. A generalized DA topology is considered, where the DAs are grouped into some geographically dispersed ports within each of which the DAs are co-located. Assuming equal power allocation within each port, the outage probability is derived. We find that minimizing the outage probability only requires the feedback of the eigenvalues of the transmit correlation matrix at the transmitter. Since it is computationally intensive to minimize the outage probability, an antenna subset selection with suboptimal power allocation scheme is proposed, whose effectiveness has been demonstrated by numerical results

This paper studies downlink transmits antenna selection with power and rate allocation in generalized distributed wireless communication systems (G-DWCS). Based on the large-scale fading statistics (path loss and shadow fading) at the transmitter, antenna selection criteria have been proposed for combined space time block code (STBC) and Vertical Bell-Labs Layered Space Time (V-BLAST) structure, where each layer of V-BLAST is composed of co-located multiple antennas for independent STBC transmission. The sub-optimal antenna set, sub-optimal power and rate allocation are determined to minimize an upper bound on the symbol error rate (SER). At the receiver a fixed detection order is followed which is obtained meanwhile in the antenna selection process at the transmitter. It's verified by Monte Carlo simulation that our proposed antenna selection criteria can judiciously select the optimal antenna set and allocate proper power and rate, and are very suitable for G-DWCS system.

In this paper, output SINR performance of a smart antenna and two kinds of 2D-RAKE technology - combined and joint spatial-temporal 2D-RAKE - is compared in the same single cell DS-CDMA system. Average output SINR expressions are derived, with the parameters analyzed and performance compared.