In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

Efficient resource utilization is vital for terrestrial and non-terrestrial networks (TN-NTN) integration. However, different spatio-temporal resource scales in TN and NTN networks pose challenges for joint resource allocation. To tackle this problem, we propose a joint user scheduling and multi-domain resource allocation scheme in the downlink network, to improve coverage for ground users (GUs). Specifically, the scheme is designed in two time-scales, including large-scale satellite beam-hopping (i.e., spatial resource allocation) and small-scale time-frequency resource allocation. For beam-hopping, we first analyze the coverage of terrestrial base stations (TBS) for GUs, and accordingly propose a joint design of user scheduling and beam-hopping. For time-frequency resource allocation, to cope with the complexity induced by multi-domain and multi-scale resources, we propose a two-step approach which first obtains a preliminary allocation with worst-case co-frequency interference assumption, then employs the genetic algorithm to re-allocate redundant resources, thereby increasing the proportion of successfully served GUs. We evaluate the performance of the proposed scheme through simulations with different user demands and network service settings. Results show that the proposed scheme provides considerable improvement over existing schemes, which can efficiently reduce co-frequency interference and provide service for more GUs.

The reconfigurable intelligent surface (RIS) has been widely studied in far-field communications (FFC), and further extended to near-field communications (NFC). With the distinct electromagnetic properties in FFC and NFC, it remains debatable whether the RIS deployment strategies established for FFC are still applicable to NFC. To bridge this gap, we examine RIS-aided cooperative relaying in NFC through two representative configurations, single-RIS and multi-RIS relaying, in which a decode-and-forward relay forwards data from the source to the user with the assistance of RIS For the single RIS deployment, we show that the achievable rates with RIS deployed near the base station (BS) or near relay are completely different. It is revealed that, with a single-antenna relay, the multi-RIS is significantly preferable over the single RIS in NFC without the requirement of massive RIS reflection elements, which is completely different from that of FFC. Furthermore, we derive closed-form expressions for the achievable rates under both single-antenna and multi-antenna relay configurations to explicitly determine the distance threshold. The presented analytical results demonstrate that locating RIS near the relay yields substantial performance gains when the transmission distance exceeds a certain threshold. This conclusion is validated by numerical simulations, which systematically illustrates the distinct impact of RIS deployment strategies in near-field versus far-field regions.

Maritime channel modeling is crucial for designing robust nearshore communication systems, yet reliable models that account for the dynamic marine environment with varying sea waves, wind conditions, and vessel motions remain scarce. This article investigates land-to-ship maritime wireless channel characteristics at 5.8 GHz based upon an extensive measurement campaign, with concurrent hydrological and meteorological information collection. First, a novel large-scale path loss model with physical foundation and high accuracy is proposed for dynamic marine environments. Then, we introduce the concept of sea-wave-induced fixed-point (SWIFT) fading, a peculiar phenomenon in maritime scenarios that captures the impact of sea surface fluctuations on received power. An enhanced two-ray model incorporating vessel rotational motion is propounded to simulate the SWIFT fading, showing good alignment with measured data, particularly for modest antenna movements. Next, the small-scale fading is studied by leveraging a variety of models including the two-wave with diffuse power (TWDP) and asymmetric Laplace distributions, with the latter performing well in most cases, while TWDP better captures bimodal fading in rough seas. Furthermore, maritime channel sparsity is examined via the Gini index and RicianKfactor, and temporal dispersion is characterized. The resulting channel models and parameter characteristics offer valuable insights for maritime wireless system design and deployment.

To reduce the computational complexity of near-field channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, the concept ofvirtual cell subarraysin subarray hybrid precoding architectures, is firstly given. Multiple subarrays with strong correlation can be flexibly and dynamically combined, to jointly extract relevant features of the channels. Based on this, an undersampling matching and oversampling refinement pursuit (UMORP) algorithm is proposed, which can detect the channel parameters of cell subarrays through an undersampling dictionary constructed by analog phase shifters. This approach facilitates the estimation of the channel of a single cell subarray with much fewer pilots and lower hardware capability requirement. Then, a multiple-path decoupled spatial extrapolation (MPDSE) scheme is proposed for fully dimensional channel reconstruction, which orthogonally decouples multiple paths from the received signal of a single cell subarray firstly, and then utilize the spatial correlation between adjacent cell subarrays to extrapolate the channels with low cost. Moreover, to further reduce the computational complexity in XL-MIMO systems, a spatial multiple cell subarrays joint extrapolation (SMCJE) scheme is also proposed. Based on the spatial geometry among cell subarrays, only several cell suabrrays are utilized to jointly estimate the distances and reconstruct the fully dimensional near-field channel, achieving a low level of computational complexity. Our simulation results verify that the proposed schemes perform better in XL-MIMO systems, while requiring fewer pilots and exhibiting much lower computational complexity.

In this paper, we investigate the impact of random wobbling on the performance of unmanned aerial vehicle (UAV) communications. We consider two practical implementation forms of UAV-multiple input multiple output (MIMO) system, namely 1) centralized implementation, where a compact multi-antenna array is deployed on a single UAV platform and hence wobbling directly has impact on the entire array; 2) distributed implementation, where a virtual MIMO system is formed via cooperative UAV swarm, and wobbling occurs independently for every single transmit antenna. For both cases, we define and analyze the beamforming gain loss-factor as the performance metric to evaluate the adverse effect caused by wobbling. Based on derived analytical expressions, the centralized and distributed implementations are compared to show their preferable operating scenarios, respectively, considering different system and implementation parameters including the carrier frequency, wobbling variance, user position, antenna number, and inter-antenna spacing, etc. It is revealed that the distributed MIMO implementation could be more robust to wobbling, especially when the number of antennas is large. Simulations verify the accuracy of derived analytical expressions, and confirm the corresponding conclusions.

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results demonstrate that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios.

Spectrum sharing among the satellite, unmanned aerial vehicle (UAV), and terrestrial components is crucial to alleviate spectrum scarcity in a hybrid maritime communication network (MCN). In the time domain, spectrum sharing optimization based on fine-grained time slices is widely envisioned. However, fine-grained time synchronization is rather challenging due to the large diversity in the link delay. In this paper, we focus on link-cluster-based spectrum sharing based on coordinated link scheduling in terms of subcarrier and time slice allocation. By link-cluster-based scheduling for the satellite links, time-slice-oriented spectrum sharing is realized with coarse time synchronization at time scales much larger than single time slice duration. Only large-scale channel state information (CSI) is utilized for saving cost. An NP-hard mixed integer programming (MIP) problem is formulated, and with the aid of link clustering, a suboptimal spectrum sharing scheme, with only a small performance gap to the optimal one, is proposed. Simulations show that a significant improvement in both energy efficiency and spectrum efficiency could be achieved by the proposed scheme.

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this paper, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image to image (I2I) inpainting task, which predicts the channel knowledge at specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form frequency band decomposition structure of the LP, we design tailored subnetworks for different frequency components. In addition, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Experiments demonstrate that the proposed scheme can accurately reconstruct the CKM with low computational complexity. Moreover, the proposed method has a strong generalization ability to be implemented in different wireless communication scenarios.

Visibility region (VR) is known as a key channel characteristic appeared in extra-large massive MIMO (XL-MIMO) systems, which can be exploited to facilitate low-complexity transmission design. Existing VR recognition method requires an a priori location-Vrdataset, with which a user's VR can be estimated given its location. This dataset is constructed by selecting some beacon users (BUs) to estimate the VR at their locations via uplink training. Constrained by the available training resource and possible environmental variation, practical size of the dataset is usually limited; how to efficiently select BUs for better VR recognition accuracy is therefore important. To this end, we propose and compare three BU selection methods, including random selection, minimum spacing constrained (MSC) selection, and a more sophisticated method (denoted as dynamic boundary refining, DBR) which utilizes partial of BUs for exploring unknown environment, while selecting the other BUs for further refining already-estimated VR region boundaries. Simulation results show that with a small number of BUs, both MSC and DBR achieve similar VR recognition performance and outperform random selection; as the number of BUs becomes larger, DBR achieves the best recognition accuracy.

The utilization of unmanned aerial vehicles (UAVs) for efficient data collection has gained considerable attention. In this paper, we examine a scenario involving grant-free access from Internet of Things (IoT) devices, where the random access may cause packet collision, stemming from multiple devices concurrently transmitting data. To address this issue, we propose a deep reinforcement learning-based collision avoidance (DRL-CA) approach for UAV data collection, which optimizes the UAV trajectory. The approach assists UAVs in identifying and maximizing the acquisition of device packet in an environment characterized by probabilistic packet transmission and potential collisions among device packets while ensuring a timely arrival at the destination. Through simulations, our proposed method effectively mitigates unnecessary conflicts among device packets while achieving the optimization objective.

By decoupling the dedicated radio frequency (RF) chain into transmit RF (TX RF) chain and receive RF (RX RF) chain, the asymmetrical system can flexibly equip the downlink/uplink array with different number of TX/RX RF chain according to the practical demand in a massive multiple-input multiple-output Internet of Things (IoT) network. To reduce cost and power consumption, this paper maximizes the uplink resource efficiency (RE) under Weichselberger channel model by designing transmit covariance matrices and receive antenna selection (RAS). In IoT networks with multiple IoT nodes, we propose an alternate optimization algorithm to iteratively optimize transmit covariance matrices and RAS by exploiting statistical channel state information. Specifically, for correlated channels, we propose a penalty method-based algorithm for RAS which utilizes Dinkelbach’s transform and linear relaxation to tackle the intractable fractional function and binary constrain, respectively. Compared with greedy search, the proposed algorithm has lower complexity without much loss of performance. For independent identically distributed channels, we simplify the RE maximization problem and provide the necessary conditions of the optimal number of receive antennas and transmit power. Finally, the validness of our conclusions as well as the effectiveness of proposed algorithms are illustrated by numerical simulations.

In this paper, an efficient near-field channel estimation algorithm, and a novel cascade channel reconstruction scheme, with significantly reduced pilot overhead and computational complexity, are proposed for hybrid extra large-scale reconfigurable intelligent surface (XL-RIS)-aided multi-input multi-output (MIMO) systems. A unique hybrid XL-RIS architecture is devised, in which the elements at the designed central subarray, and many specially selected discrete elements, are active, while others are passive. Meanwhile, a damped Newtonized orthogonal matching pursuit algorithm combining the planar and spherical wave models (DNOMP-CPSW) is proposed, in which the angle and distance parameters of multipaths are estimated through the received signals of the central subarray and the discrete active elements respectively, and the near-field channel can be reconstructed accurately with low pilot overhead and computational complexity. Moreover, to decrease the cost of cascade channel reconstruction in the considered system, a separate channel estimation scheme based on the decoupling operation (SCEDO) is proposed, which estimates the two separate channels with only 3 pilots and reduced computational complexity, and then reconstruct the cascade channel. Furthermore, the phase shift strategy of the XL-RIS with 2-bits quantization is devised, which can increase the energy of the received signal and maintain the set order to estimate multipaths in different stages of the SCEDO scheme, to improve the accuracy of the estimates, and enhance the reliability of the separate channel estimation. Simulation results verify that the proposed DNOMP-CPSW algorithm and the hybrid XL-RIS phase shift strategy can enhance the performance of the considered system. Compared with other schemes, the SCEDO scheme can reconstruct the cascade channel efficiently, with much reduced pilot overhead and computational complexity.

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users’ signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transmission Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP calledPEPescwhich has two distinctive features. First, it featuresretransmission-freeloss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higherandsmoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications includingiperfand HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

Spectrum sharing among the satellite, unmanned aerial vehicle (UAV), and terrestrial components is crucial to alleviate spectrum scarcity in a hybrid maritime communication network (MCN). Fine-grained spectrum sharing based on ms-level time-domain slices is widely envisioned. However, ms-level time synchronization is challenging in the hybrid MCN due to the large diversity in the link delay. To tackle this challenge, we propose a fine-over-coarse spectrum sharing framework based on coordinated link scheduling, which is realized by joint subcarrier and time slice allocation. Specially, by link-cluster-based scheduling with grouped time slice allocation for the satellite links, time-slice-oriented spectrum sharing is realized with coarse time synchronization at time scales much larger than a single time slice duration. In the framework, only large-scale channel state information (CSI) is utilized for saving cost. A worst-case model is introduced to depict interference caused by satellite link clusters, and an NP-hard mixed integer programming (MIP) problem is formulated. Based on analysis on the characteristics of the optimal solution, a novel link clustering algorithm is proposed to form a group of shaped virtual cells within the coverage area of the MCN. A suboptimal spectrum sharing scheme with only a small performance gap to the optimal one is then proposed. Simulations show that a significant improvement in both energy efficiency and spectrum efficiency can be achieved by the proposed framework.

We investigate a new deployment form of reflective intelligent surface (RIS), which aims at enhancing the quality of service of a main communication system in a target region, while without the need of changing its transmission protocol and scheme (i.e., the RIS is “transparent” to the main system). To this end, we mathematically formulate a coverage enhancement problem, where a RIS is used transparently in the sense that the BS can be unaware of its existence, while the minimum channel link strength, measured from every BS antenna to any point in the target region, can be maximized. The formulated problem is non-convex with mixed discrete-continuous variables. To tackle this challenge, we recast it into a convex feasibility problem via spatial sampling and semi-definite relaxation. Based on a derived analytical upper bound on the link strength difference between any two location points, we further characterize the coverage-similarity region of a given location, and accordingly propose an improved spatial sampling scheme for efficient implementation. Simulation results show that the proposed transparent RIS design achieves better coverage performance than benchmark schemes. More importantly, it can effectively improve the communication performance without affecting the transmission scheme originally adopted by the main communication system.

Constrained application protocol (CoAP) for low-power low-rate data transport in Industrial Internet of Things (IIoT) networks is typically running with two modes, namely, confirmable mode and nonconfirmable mode, respectively. Confirmable mode relies on retransmission to ensure a guaranteed Quality of Service (QoS) in terms of packet loss rate at the cost of increased latency and power consumption. Whereas nonconfirmable mode consumes less power, it is known to be packet loss prone. To enrich the CoAP transport for IIoT networks, we propose a packet-level forward error correction (FEC) mechanism based on systematic coding with an adaptive code rate to provide energy efficient and reliable packet delivery. We mathematically analyze packet loss and cost of energy consumption of the proposed mechanism and compare it with the confirmable CoAP transport scheme in the Gilbert–Elliott channel model. We demonstrate that the proposed mechanism can enhance the performance of nonconfirmable CoAP to be comparable to confirmable CoAP in terms of packet loss rate, while outperforming it in energy consumption. The analytical and simulation results verify that the proposed mechanism is suitable for IIoT networks especially in high-erasure burstiness scenarios.

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA.

As the 6G network is evolving towards a space-air-ground integrated scale with ubiquitous long-distance non-terrestrial network (NTN) links, packet-level streaming forward erasure correction (FEC), which can achieve low end-to-end in-order delivery delay over lossy links with long propagation delay, has drawn increasing interest. However, the existing streaming FEC has a problem that full-length encoding windows (EWs) including all non-acknowledged source packets are used when generating repair packets, which incurs high computational cost when the link’s bandwidth-delay product is large. To address the problem, this paper proposes a new low-complexity streaming FEC design, where a mixture of short and full-length EWs are used. We propose a novel method to analyze the decoding window width observed by arriving repair packets, which is based on the analysis of the busy period of a virtual queue using renewal theory. Later, using the analysis as the key enabler, a design problem is formulated and solved to optimize parameters including the EW width and the fraction of short-length repair packets such that the computational cost is reduced. Evaluations using real-life code implementations show that the proposed design can significantly reduce the computational cost, while maintaining the key benefits of the original streaming FEC.

Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach’s transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.

We investigate the electromagnetic (EM) exposure constrained spectral efficiency (SE) optimization design in uplink multiuser multiple-input multiple-output (MIMO) communications assisted by the reconfigurable intelligent surface (RIS) and dynamic metasurface antennas (DMAs). By adopting the alternating optimization (AO) method, the transmit covariance, RIS phase shift, and DMA weight matrices are jointly optimized. Specifically, we propose a modified SE maximization water-filling algorithm to obtain the optimal solutions of transmit covariance matrices. Then, the optimization of the RIS phase shift matrix is addressed via exploiting the weighted minimum mean square error, block coordinate descent, and minorize-maximization methods. Furthermore, we express the closed form solution of the unconstrained DMA weight matrix optimization problem and then design the DMA weights satisfying the constraint through an AO algorithm. Numerical results indicate the effectiveness of our proposed EM exposure aware SE maximization transmission scheme over the conventional algorithms.

The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.

In the blind areas of current fifth generation (5G) networks, e.g. the remote areas, unmanned aerial vehicles (UAVs) can be used to provide on-demand connectivity. To efficiently serve the sparsely distributed users in these areas, non-orthogonal multiple access (NOMA) could be adopted to exploit the user distinguish ability in the power domain. In this paper, we consider a NOMA-based multi-UAV-aided network, where a swarm of coordinated UAVs transmit messages to unevenly distributed users through a virtual multiple-input-multiple-output (MIMO) channel. We formulate a power allocation problem to maximize the minimum user rate to assure fairness in the transmission. Different from existing studies, we use only the large-scale channel state information (CSI) in the transmission design, which characterizes the basic channel feature, and can be obtained using the location information of UAVs/users. By leveraging the random matrix theory and successive convex optimization tools, we propose an iterative algorithm to solve the problem after a series of problem transformation. Simulation results show that the proposed power allocation scheme outperforms existing methods, which shows the potential of multi-UAV-aided NOMA communications for coverage enhancement in remote areas.

This paper studies the energy efficiency (EE) optimization of a single-cell multiuser massive multiple-input multiple-output (MIMO) uplink system, where configurable dy-namic metasurface antennas (DMAs) are deployed at the base station (BS). To maximize the system EE, we present a framework for the joint optimization of the users' transmit precoding and the BS DMAs' weights, which is based on Dinkelbach's transform and an alternating optimization algorithm. Since the physical structure constraint of DMAs exhibits a non-convex form, we firstly obtain the optimal unconstrained DMAs' weights in closed form. Then, we configure those weights with the non-convex constraint and approximate them with the optimal unconstrained solutions. Our numerical results showcase that our DMAs-based systems can achieve much higher EE performance than those based on conventional antenna arrays and beamforming architectures. It is also demonstrated that the EE performance of DMAs-based uplink massive MIMO systems can be further improved by adjusting the number of microstrips and the number of meta-atoms per microstrip.

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. While on-shore base stations (BSs) constitute a key infrastructure for maritime communications, the trap of low energy efficiency caused by long transmission distances must be delicately circumvented. In this paper, we try to utilize internet of vessels (IoV) to tackle the problem. Specifically, we investigate the joint link scheduling and rate adaptation problem for a maritime communication network with both shore-to-vessel and vessel-to-vessel links, with the target of minimizing the energy consumption while assuring a quality of service (QoS) guarantee for each vessel. With only large-scale channel state information available, the problem is shown to be an NP-hard mixed integer non-linear programming problem with a group of hidden nonlinear equality constraints. A process-oriented iterative scheme is proposed based on a relaxation and gradually-approaching method following the gentlest-ascent principle, as well as the divide-and-conquer strategy. Simulation results demonstrate that the proposed scheme can achieve a prominent gain in terms of network energy consumption reduction with a rather low complexity.

Federated learning (FL) has gained considerable attention of wireless communications community owing to its nature of decentralized training and privacy-preserving. However, with limited radio resources and increasing number of user equipments (UEs), it is very hard to realize strictly synchronous model updating among all the involved UEs as required in the traditional FL algorithms. In this paper, we propose a novel asynchronous FL framework, which considers the potential failures in uploading the local models and the resultant varying degrees of staleness among the models for global update. Specifically, we first design two working modes for adapting to systems with different communication environments and tasks of different difficulty. Next, a central model fusion algorithm is designed for carefully determining the fusion weight during the global update. On one hand, it aims to make the most of the fresh information contained in the uploaded local models. On the other hand, it avoids the biased convergence by making the impact of each UE be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve the fast and smooth convergence and enhance the training efficiency significantly.

This paper investigates the downlink OFDM transmission assisted by reconfigurable intelligent surface (RIS). With single antenna implemented at both the base station (BS) and each user, we focus on the design of the phase shifts for the RIS, as well as power allocation on each subcarrier to improve the spectrum efficiency. To reduce the computation delay, we propose a deep reinforcement learning (DRL) based algorithm to optimize the RIS phase shift parameters, while allocating power on each subcarrier via water filling. Numerical results reveal that the proposed DRL-based framework can achieve a performance almost the same with that of successive convex approximation (SCA), while the computation delay can be greatly reduced.

Existing hybrid automatic repeat request (HARQ) schemes are mainly designed at bit-level, in which the bits with lower reliability are re-encoded and then re-transmitted. However, this may lead to complicated and nonflexible bit-wise joint design among all the re-transmitted packets and introduce extra decoding latency. In this paper, we propose a novel HARQ scheme with multi-packet retransmission and packet-wise polarization, MPPP-HARQ, which simply retransmits the polar coded version of the failed packets as well as the new information packets instead of any specific part of them, so as to avoid all complicated and trivial inner-bit manipulation and greatly reduce the system complexity. Just like the bit-wise polarization, the packet-wise polarization is realized by proper modulo two addition among packets. By proper combination of multiple packets at each retransmission, we can reliably recover all the received packets at the decoder using a joint iterative decoding algorithm. Simulation results show that compared to the traditional HARQ protocol, MPPP-HARQ achieves superior performance and obtains a significant performance gain in terms of throughput and error rate. Index Terms-Polar code, HARQ, joint iterative decoding algorithm, packet-wise polarization

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.

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.

In this article, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of its neighboring nodes, the number of which is progressively determined according to a heuristic searching procedure, which takes into account both the predicted convergence rate and the communication and computation costs at each iteration, resulting in a trade-off between communication and computation. Then the node chooses its neighboring nodes according to an importance sampling distribution derived theoretically to minimize the variance with the latest information it locally stores. Finally, the node updates its local information with a new update rule which adapts to the number of communication nodes. We prove the convergence of the proposed algorithm and provide an upper bound of the convergence variance brought by randomness. Extensive simulations validate the excellent performances of the proposed algorithm in terms of convergence rate and variance, the overall communication and computation cost, the impact of network topology as well as the time for evaluation, in comparison with the traditional counterparts.

In application scenarios such as Internet of Things, a large number of energy receivers (ERs) exist and line-of-sight (LOS) propagation could be common. Considering this, we investigate wireless energy transfer (WET) in extra-large massive MIMO Rician channels. We derive analytical expressions of the received net energy for different schemes, including 1) training-based WET, where the ER sends beacon signal for channel training and the energy transmitter (ET) uses the channel estimate for energy beamforming, 2) LOS beamforming, where the ET transmits to the LOS direction of the ER, and 3) energy harvesting, which allows an ER to harvest the training energy from the other ERs. We derive a path loss threshold for switching between training and LOS beamforming-based WET. We further show that the WET scheme selection of one ER is not affected by the other ERs, and the energy harvested from training is minimal in practice. With these insights, we propose an algorithm for the multi-ER scenario, which minimizes the power consumption by iteratively updating the WET scheme selection and power allocation for all ERs. Simulations show that the proposed algorithm achieves near-optimal performance as compared to exhaustive searching, while with much lower implementation complexity.

Massive machine-type communication (mMTC) is expected to provide reliable and low-latency connectivity for an enormous number of devices, which turn active sporadically or frequently. In this highly dynamic situation, it is crucial to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this article, by exploiting the large sequence space, the excellent correlation property and especially the elegant nested structure of Reed-Muller (RM) sequences, we propose a new RA scheme, which facilitates both instantaneous access for newly active users and incremental access for the existing users who suffer from detection failures. In particular, when a failure occurs, the user continues accessing the channel employing an expanded RM sequence, which is combined with the previously received ones at the access point (AP) to form a longer sequence so as to attain potentially better detection probability. Furthermore, a recursive detection algorithm is designed for jointly detecting the resultant RM sequences and the channel coefficients of both the newly active users and the existing ones. The performance of the proposed algorithm is analyzed in detail. Our simulation results validate the analysis and show the scheme's superior access probability, access latency and computational complexity.

In the mMTC scenario, enormous devices turn active sporadically or frequently to seek for opportunities to transmit short packets. In this highly dynamic situation, it is critical to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this paper, we propose an incremental RA scheme exploiting the nested Reed-Muller (RM) sequences. Specifically, the users who suffer from access failures expand their RM sequences following the given expansion rule, which utilizes both the nested structure and the cross-correlation property of RM sequences. At the receiver, a recursive detection algorithm is proposed, which exploits the discrepancy in the sequence length to detect the retransmission users progressively. On the other hand, new active users continuously spring up in the system, thus causing the incremental number of users seeking for access. In this case, the proposed scheme can detect newly active users together with retransmission ones with great detection capability and low access latency. Our simulation results verify the superior performance of the proposed RA scheme.

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes.

Data dissemination to multiple users within a predetermined deadline is a commonly required function in many emerging wireless scenarios. In this paper, we propose a data dissemination scheme enhanced by device-to-device (D2D) communications and random linear network coding (RLNC), where the broadcast and the D2D links are used simultaneously with on-the-fly RLNC to accelerate the dissemination. The probability distribution of the completion time of using either pure or systematic RLNC is analyzed assuming a finite field size, under a general model where the cooperation may occur probabilistically and multiple packets may be exchanged between the D2D users in each time slot. An optimization problem is formulated to design the cooperation parameters to minimize the expected energy consumption of the system. Numerical and simulation results show that the analysis is accurate, and that to minimize the energy consumption a tradeoff exists between allowing for longer broadcast and for more intensive cooperation. The cooperation parameters designed for multiple D2D pairs are shown to be effective for meeting the reliability requirements.

We investigate energy efficiency (EE) optimization for single-cell massive multiple-input multiple-output (MIMO) downlink transmission with only statistical channel state information (CSI) available at the base station. We first show that beam domain transmission is favorable for energy efficiency in the massive MIMO downlink, by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrix. With this conclusion, the EE optimization problem is reduced to a real-valued power allocation problem, which is much easier to tackle than the original large-dimensional complex matrix-valued precoding design problem. We further propose an iterative water-filling-structured beam domain power allocation algorithm with low complexity and guaranteed convergence, exploiting the techniques from sequential optimization, fractional optimization, and random matrix theory. Numerical results demonstrate the near-optimal performance of our proposed statistical CSI aided EE optimization approach.

We investigate energy efficiency (EE) optimization for massive multiple-input multiple-output (MIMO) transmission in a single cell downlink scenario where the base station has only access to statistical channel state information (CSI) of the user terminals. To maximize the system EE, we first figure out a solution for the eigenvectors of the optimal transmit covariance matrices in a closed form. Notably, such a solution indicates that it is more favorable to perform energy efficient transmission in the beam domain for massive MIMO downlink, by which we reformulate the original complicated EE optimization precoding design to a simpler power allocation problem in the beam domain. Exploiting the approaches of sequential optimization, fractional optimization, and deterministic equivalent, we further propose an iterative algorithm for power allocation in the beam domain with guaranteed convergence to a stationary point. Numerical results demonstrate the superior performance and the fast convergence of our proposed statistical CSI aided EE optimization approach for massive MIMO downlink.

In this paper, we propose an uplink joint time and angle domain sparse code multiple access (TASCMA) scheme for mmWave systems to improve the system capacity and user con⌉nectivity. This proposed scheme mainly includes three processes: sparse spreading, antenna allocation and beamforming, which are coordinated by the base station (BS). The spreading process is conducted over time domain by associating a specific sparse spreading vector with each user according to its location, while the antenna allocation and beamforming processes are conducted over angle domain to superimpose the user signals in a beam to further achieve the multiplexing gain. At each user, it spreads its signal with its own spreading sequence and then sends the spread signal to the mmWave channel. At the BS receiver, first the receive beamforming is performed and then a corresponding factor graph is constructed, based on which, message passing algorithm (MPA) is performed to recover all users' transmitted messages. Simulation results show that the proposed TASCMA scheme has better Bit Error Rate(BER) performance than the existing schemes. Keywords-TASCMA, Sparse spread spectrum, Antenna allo⌉cation, Beamforming, MPA.

We consider an underlay wiretap cognitive two-way relay network (CTWRN), where two secondary sources exchange their messages via multiple secondary decode-and-forward digital network coding relays in the presence of an eavesdropper by using a three-phase time division broadcast protocol and sharing the licensed spectrum of primary users. To mitigate eavesdropping attacks, an artificial noise (AN)-aided opportunistic relay selection scheme, called generalized max-min (GMM) relay selection is proposed to enhance physical layer security for the wiretap CTWRNs. The performance of the GMM scheme is analyzed, and evaluated by the exact closed-form outage probability and intercept probability. Additionally, we also provide asymptotic approximations for the outage probability and intercept probability at high signal-to-noise ratio. For comparison, we analyze the performance of the conventional max-min (MM) relay selection scheme as well. It is shown that the GMM scheme outperforms the MM scheme in terms of the security-reliability tradeoff (SRT), where the security and reliability are quantified by the intercept probability and outage probability, respectively. Moreover, the SRTs of the MM and GMM schemes can be substantially improved by increasing the number of secondary relays, while the improvement of the GMM scheme is more evident than that of the MM scheme.

Reed-Muller (RM) sequences have been widely used in compressed sensing (CS) to construct deterministic measurement matrices and extract attributes of sparse signals. However, if the signal is not sparse enough, the existing reconstruction algorithms encounter serious performance degradation. In this paper, invoking the elegant nested structure of second-order RM sequences, a soft-decision reconstruction algorithm is proposed. With the soft- information passing through the nested structure, the proposed reconstruction algorithm outperforms the existing ones. Notably, the performance can be further improved based on shuffling operations. Numerical results verify the good performance of the proposed algorithm and show that it preserves quite low computational complexity.

Relay transmissions play an important role in many types of communication systems. In this paper, we consider packet-level coded transmissions over lossy relay links with the finite-buffer and coding coefficients delivery cost constraints. We propose a low-complexity coding scheme where packets are encoded from sequentially formed random subsets of source packets called batches. The relay recodes only from the buffered packets belonging to the same batch to maintain the code sparsity, lowering the packet header overhead and the decoding complexity compared to the random linear network coding (RLNC). To analyze the performance, we first propose an absorbing Markov chain model to analyze the RLNC transmission over finite-buffer relay links. The finite-length analysis not only provides a lower bound on the completion time using any sparser random codes but also characterizes each individual batch's transmission of the proposed code. Based on the analysis, another Markov chain is proposed to determine the decoding failure probability and the expected completion time of the batch coding scheme. As shown through analysis and simulations, the proposed scheme achieves higher effective end-to-end rates than RLNC when the coding coefficients delivery cost is considered and is also with much lowered decoding complexity thanks to its sparseness.

In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we show analytically that a generalized singular value decomposition (GSVD) based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio (SNR) regime. In light of this, we propose a novel Per-Group-GSVD (PG-GSVD) design which can effectively compensate the performance loss caused by the GSVD design. More importantly, the computational complexity of the PG-GSVD design is by orders of magnitude lower than that of the existing design for finite alphabet inputs in \cite{Wu2012TVT} while the resulting performance loss is minimal. Numerical results indicate that the proposed PG-GSVD design can be efficiently implemented in large-scale multiple-antenna systems and achieves significant performance gains compared to the GSVD design.

In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we investigate the case where instantaneous channel state information (CSI) of the eavesdropper is known at the transmitter. We show analytically that a generalized singular value decomposition (GSVD)-based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio regime. In light of this, we propose a novel Per-Group-GSVD (PG-GSVD) design, which can effectively compensate the performance loss caused by the GSVD design. More importantly, the computational complexity of the PG-GSVD design is by orders of magnitude lower than that of the existing design for finite alphabet inputs while the resulting performance loss is minimal. Then, we extend the PG-GSVD design to the case where only statistical CSI of the eavesdropper is available at the transmitter. Numerical results indicate that the proposed PG-GSVD design can be efficiently implemented in large-scale multiple-antenna systems and achieves significant performance gains compared with the GSVD design.

In this paper, we consider the uplink of a dense small cell network (SCN) using pilot reuse in channel training and maximum ratio combining (MRC) for data detection. Taking into account imperfect channel state information (CSI) caused by pilot contamination, we derive exact closed-form expressions of the per-user achievable ergodic rate with arbitrary pilot reuse factors. After that, we first reveal that the user terminals, which are geographically separated with large distance between each other, can reuse pilot, suffering only low pilot contamination. Based on this insight, we further propose a low-complexity pilot reuse algorithm based on the minimum sum of estimation error criterion. Simulation results verify our theoretical analysis and demonstrate that the proposed pilot reuse algorithm is very effective in suppressing pilot contamination in SCN.

We investigate the achievable ergodic sum-rate of multi-user multiple-input multiple-output systems in Ricean fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by utilizing the Mullen's inequality, which is then used to analyze the effect of channel mean information on the achievable sum-rate. With these results, a novel statistical-eigenmode space-division multipleaccess downlink transmission scheme is proposed. For this scheme, we derive an exact closed-form expression for the achievable ergodic sum-rate. Our results show that the achievable ergodic sum-rate converges to a saturation value in the high signal-to-noise ratio (SNR) region and reaches to a lower limit value in the lower Ricean K-factor range. In addition, we present tractable upper and lower bounds, which are shown to be tight for any SNR and Ricean K-factor value. Finally, the theoretical analysis is validated via numerical simulations.

In this paper, we investigate the achievable ergodic sum-rate for multiuser massive multiple-input multiple-output systems, where different array configurations such as uniform linear arrays (ULAs), uniform planar arrays (UPAs) and uniform circular arrays (UCAs), are deployed at the base station (BS). The investigation is carried out based on a three dimensional spatial propagation channel model by taking into account both the azimuth and elevation angular domains. We first investigate the value of the inner product of two channel vectors, for which closed-form expressions are derived with different array configurations in line-of-sight channel. After that, the effects of the BS antenna number, the angles of departure of users, and the inter-antenna spacing on the inner product, further, on the achievable ergodic rate, are investigated. Finally, theoretical results are verified via numerical simulations, which is shown that deploying ULAs outperforms the other array configurations (UPAs and UCAs) in terms of improving the achievable ergodic rate and the larger inter-antenna spacing can contributes substantially to the achievable sum-rate.

Two‐way relaying systems efficiently accomplish transmissions in both directions within dual‐hop, hence, two time slots can be saved compared with one‐way relaying. However, the conventional two‐way relaying protocol requires the assumption of symmetric traffic demands, that is, each transmitter node has to act as a receiver in latter slot. This assumption restricts applying two‐way relay to general and practical scenarios. In this study, the authors release this unpractical constraint by assuming that the transmitter in slot 1 and receiver in slot 2 can be any nodes, which are not necessarily being the same. For this scenario, a novel transmission protocol exploiting the overhearing link to suppress the interference caused by asymmetric traffic, denoted as overhearing transmission, is proposed. With the overhearing transmission protocol, and in the light of green communications, the precoding matrices at the decode‐and‐forward multi‐antenna relay are optimised to improve energy efficiency in both uplink and downlink (DL) transmission directions, where the objective is to minimise the transmit power at the relay while guaranteeing a target transmission rate. The authors transform the original non‐convex problem to an equivalent form, which can be readily solved by typical semi‐definite relaxation approaches. An efficient algorithm is further proposed to implement the precoding design in practice. Simulation results show that the proposed algorithm is able to minimise the power consumption at the relay, with the minimum rate constraints of both the uplink and DL transmissions being satisfied.

This study investigates the scheduling problem for a single‐cell downlink distributed antenna systems (DASs) with a massive number of remote access units (RAUs). To reduce signalling overhead under limited backhaul capacity, the authors make use of local long‐term channel state information (CSI) in coordinated scheduling design. They first derive the ergodic rate expressions for both the single RAU transmission and the cooperative RAU transmission modes as functions of long‐term CSI. Then greedy scheduling algorithms (GSAs) aiming for the maximum ergodic sum rate for the massive DAS using long‐term CSI are proposed. To mitigate the intra‐cell interference, a two‐stage GSA with hybrid transmission mode is devised. Asymptotic analysis reveals that as the number of RAUs goes to infinity, intra‐cell interference can be effectively mitigated. Simulation results verify the analysis and demonstrate that the two‐stage GSA exhibits a higher ergodic sum‐rate.

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen's inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SE-SDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the impact of the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician $K$-factor, on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician $K$-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zero-forcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician $K$-factor regime.

In this paper, we investigate the artificial noise-aided jamming design for a transmitter equipped with large antenna array in Rician fading channels. We figure out that when the number of transmit antennas tends to infinity, whether the secrecy outage happens in a Rician channel depends on the geometric locations of eavesdroppers. In this light, we first define and analytically describe the secrecy outage region (SOR), indicating all possible locations of an eavesdropper that can cause secrecy outage. After that, the secrecy outage probability (SOP) is derived, and a jamming-beneficial range, i.e., the distance range of eavesdroppers which enables uniform jamming to reduce the SOP, is determined. Then, the optimal power allocation between messages and artificial noise is investigated for different scenarios. Furthermore, to use the jamming power more efficiently and further reduce the SOP, we propose directional jamming that generates jamming signals at selected beams (mapped to physical angles) only, and power allocation algorithms are proposed for the cases with and without the information of the suspicious area, i.e., possible locations of eavesdroppers. We further extend the discussions to multiuser and multi-cell scenarios. At last, numerical results validate our conclusions and show the effectiveness of our proposed jamming power allocation schemes.

This paper investigates the scheduling problem for a single-cell distributed multiple-input multiple-output (d-MIMO) downlink system with a massive number of remote access units (RAUs), N. We first derive the ergodic rate expressions for both the single RAU transmission (SRT) and the cooperative RAU transmission (CRT) modes as functions of long-term channel state information (CSI). Then, greedy scheduling algorithms aiming for maximizing the ergodic sum rate for the massive d-MIMO system using local long-term CSI are proposed. To mitigate intra-cell interference, a two-stage greedy scheduling algorithm (GSA) is developed to further improve the ergodic sum rate. Asymptotic analysis reveals that with infinite N intra-cell interference can be efficiently mitigated. Simulation results verify the derived expressions and demonstrate that the two-stage GSA exhibits a higher ergodic sum rate.

This paper proposes a new codebook structure called Kronecker-product based codebook (KPC), where each codeword is the Kronecker product of two oversampled DFT codewords in both the horizontal and vertical domains. The KPC is especially suitable for the three-dimensional (3D) multiuser multi-input multi-output (MU-MIMO) systems. Besides, channel state information feedback based on the best companion cluster scheme is investigated. Since all codewords have been grouped into several clusters, each user feeds back its best precoding matrix index, best interference cluster index and channel quality information, then the BS pairs and schedules users according to the received feedback. Different codewords clustering methods affect the performance of the limited feedback schemes. We proposes two kinds of codewords clustering methods based on 3D beam patterns, including both the symmetric and asymmetric one. Simulation shows that with properly clustered codewords, our proposed 3D MU-MIMO feedback scheme has a significant throughput gain against 2D MU-MIMO feedback scheme.

We propose a multiuser downlink transmission scheme exploiting both statistical and outdated channel state information (CSI) at the transmitter. Based on the outdated CSI-aided transmission scheme introduced in (denoted as MAT), the proposed scheme reduces the original K-user MAT system to a two-user virtual MAT system, through statistical precoding in the first two transmission slots. Thus, the proposed scheme (denoted as V-MAT) reduces efficiently the implementation complexity, while increasing the achievable rate at finite signal-to-noise ratios (SNRs). For the V-MAT scheme, we derive an analytical high SNR rate approximation for correlated Rayleigh fading. Furthermore, for independent and identically distributed Rayleigh fading, we derive an exact rate expression at high SNRs, as well as a tight lower bound which applies for arbitrary SNRs. Then, precoder design is investigated, where an efficient near-optimal solution is proposed for arbitrary number of transmit antennas, and a closed-form optimal solution is derived for the two-antenna case. It is demonstrated that the proposed V-MAT scheme yields higher achievable rate than the original MAT scheme at practical SNRs. Moreover, by combining the V-MAT scheme and the generalized MAT scheme of , where precoding is implemented in the third transmission slot, the achievable rate can be further increased.

This paper proposes a statistical-eigenmode spacedivision multiple-access (SE-SDMA) transmission for a two-user downlink system where two transmit antennas are equipped at the base station and each mobile user has one receive antenna, assuming that only statistical channel state information (CSI) is available at the transmitter. By maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio, the proposed SE-SDMA approach selects two users with orthogonal principal statistical eigen-directions and transmits to each user along the corresponding eigenmode. We derive an exact expression of the ergodic achievable rate, and compare it with the zero-forcing beamforming (ZFBF) system exploiting instantaneous CSI. It is shown that SE-SDMA can achieve the maximum ergodic sum-rate of the two selected users, and provide significant user selection gain. Analytical and simulation results show that the rate gap between SE-SDMA and ZFBF with perfect CSI at the transmitter can tend to zero in highly correlated channels, which indicates that statistical precoding can be used instead of instantaneous precoding in certain environments.

In this paper, we study adaptive transmission mode switching between statistical and instantaneous channel state information (CSI) aided single-user (SU) and multiuser (MU) precoding for a two-user downlink system, where two transmit antennas are equipped at the base station and each mobile user has one receive antenna. In the case where only statistical CSI (SCSI) is available at the transmitter, a statistical-eigenmode space-division multiple-access (SE-SDMA) scheme is proposed by maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio. An exact analytical expression of the ergodic achievable rate is derived for the proposed SE-SDMA and compared with SU schemes such as SE transmission (SET) and instantaneous CSI (ICSI)-aided beamforming (BF), as well as the MU schemes such as ICSI-aided zero-forcing BF (ZFBF). Assuming the ICSI obtained at the transmitter is imperfect, the operating regions of these schemes are determined for different signal-to-noise ratio regions, channel correlation levels and ICSI inaccuracy levels.

This paper proposes limited feedback spatial division multiple access (SDMA) schemes for transmit correlated channels by using statistical channel state information (SCSI) and instantaneous channel state information (ICSI). Different from conventional codebook-based MU-MIMO scheme, the proposed multi-beam selection with single channel quality indicator (CQI) feedback (MBS-SCF) scheme determines the preferred beam vector by exploiting the SCSI and only feeds back CQI at each timeslot. The performance of the MBS-SCF scheme is nearly the same as the conventional scheme. In order to further improve the sum rate, we propose multi-beam selection with dual CQIs feedback (MBS-DCF) scheme, which determines dual statistical eigen-directions and feeds back dual CQIs at each timeslot. It will increase the opportunity to exploit multiuser diversity and multiplexing gain. Simulation results demonstrate that the MBS-DCF scheme exhibits a higher sum rate than the conventional scheme does.

A novel 3D cluster-based double-directional MIMO channel model with consideration of polarization is proposed and the modeling process is described in detail. The spatial channel model (SCM) proposed by 3GPP (3rdGeneration Partnership Project) is retrieved and compared with the new model. Based on the new model, capacity and spatial correlation of the co-located dual- and triple-polarized MIMO systems are simulated through Monte-Carlo method and the results are analyzed. Simulation results reveal that triple-polarized antennas can be deployed in MIMO systems to improve the channel capacity remarkably in rich scattering environments. The impact of the azimuth and elevation angle spread (AS) on the channel correlation is also analyzed by simulation at the end of the paper.