Sixth-Generation (6G) networks necessitate intelligent and energy-efficient operations. However, the direct deployment of Large Language Models (LLMs) for real-time 6G control is hindered by significant latency and energy constraints, conflicting with green 6G imperatives. This paper pioneers a collaborative architecture of large-small AI models to address these challenges. In this architecture, resource-intensive LLMs, i.e., large models, are strategically employed offline for comprehensive Wireless Data Knowledge Graph (WDKG) construction, effectively distilling domain knowledge. This WDKG, in turn, enables the development and deployment of lightweight, efficient small models for real-time network tasks by facilitating the generation of optimized feature datasets. To operationalize this, we first introduce a novel multi-agent collaborative LLM framework, bolstered by an enhanced semantic representation method incorporating domain-adaptive embedding fine-tuning and mutual information (MI)-based feature encoding, for automated high-fidelity WDKG construction. Subsequently, we propose the Semantic-Data and Spatio-Temporal (SD-ST) model, which uniquely fuses LLM-extracted semantic information with the spatio-temporal characteristics of wireless network data and WDKG structure. Insights from the SD-ST model guide a WDKG-driven method for generating optimized feature datasets by evaluating node influence and redundancy. Experimental validation demonstrates that these distilled feature datasets lead to substantial reductions in training and inference overhead for the lightweight downstream AI models, offering a tangible pathway towards greener, more efficient, and intelligent 6G networks.

Delay guarantee is essential for wireless communication technology, which is beneficial for real-time data processing. Such guarantee manifests as hard delay constraint, whose resolution facilitates reliable task fulfillment within strict deadline while enabling efficient communication resource scheduling. In this paper, we investigate the problem of transmitting a certain amount of data within a deadline and optimizing the expected total energy in an Orthogonal Frequency Division Multiplexing (OFDM) system. We represent the problem as a finite-horizon stochastic dynamic optimization problem, and aim to derive decision rule for allocating communication transmission rates across subcarriers. We propose a method named Multicarrier Deterministic Approximation Method (MDA). First, to address the complexity of expectation computation, we approximate the expected value function. Subsequently, for the resulting deterministic optimization problem, we introduce auxiliary variable, and adopt low-complexity two-layer optimization framework. For the proposed method, we derive performance upper bound for deterministic parameter with specific value and asymptotic performance upper bound for deterministic parameter with general value. The performance bound theoretically demonstrates the superiority of the proposed method over both the equal rate method and the first slot method. Furthermore, it proves that increasing the deterministic parameter under relaxed delay constraint can achieve enhanced theoretical performance guarantee. Finally, the effectiveness of the proposed method is validated through simulations.

The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data. However, in the context of decentralized data access within Web3.0, it is imperative to cope with efficiency concerns caused by the replication of redundant data, as well as security vulnerabilities caused by data inconsistency. To address these challenges, we develop a Trustworthy Decentralized Cooperative Caching (TDC-Cache) framework for Web3.0 to ensure efficient caching and enhance system resilience against adversarial threats. This framework features a two-layer architecture, wherein the Decentralized Oracle Network (DON) layer serves as a trusted intermediary platform for decentralized caching, bridging the contents from decentralized storage and the content requests from users. In light of the complexity of Web3.0 network topologies and data flows, we propose a Deep Reinforcement Learning-Based Decentralized Caching (DRL-DC) for TDC-Cache to dynamically optimize caching strategies of distributed oracles. Furthermore, we develop a Proof of Cooperative Learning (PoCL) consensus to maintain the consistency of decentralized caching decisions within DON. Experimental results show that, compared with existing approaches, the proposed framework reduces average access latency by 20%, increases the cache hit rate by at most 18%, and improves the average success consensus rate by 10%. Overall, this paper serves as a first foray into the investigation of decentralized caching framework and strategy for Web3.0.

Exploiting on-device data and computing power for machine learning at the network edge is challenged by constrained device resources, privacy requirements, and local data heterogeneity. To address the above gap, this work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized ML. BagChain integrates blockchain with distributed ML by replacing the computationally costly hash computing in proof-of-work with ML model training and validation, and does not rely on any trusted central servers. Individual miners in BagChain train base models by using their local computing resources and private data and further aggregate these base models, which could be very weak, into strong ensemble models. More specifically, we design a three-layer blockchain structure and associated generation and validation mechanisms to enable distributed ML among uncoordinated miners without revealing raw data. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delay and limited bandwidth. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various ML tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing resource constrained mobile devices, heterogeneous private user data, and limited network connectivity. The source code of BagChain is released at: https://github.com/czxdev/BagChain.

Optical wireless communication (OWC), with its abundant spectrum resources enabling ultra-high data rates, has emerged as a promising technique for the sixth generation (6G) wireless communications. To address the challenge of aligning base stations (BSs) and user terminals (UTs), as well as to enhance the number of served UTs and transmission rates per UT, this paper proposes a fiber-enabled network massive multiple-input multiple-output (MIMO) OWC system. By employing distributed passive optical antennas (POAs) comprised of fiber port arrays and lenses, BSs generate optical beams irradiating towards different directions, which can provide the optical signal coverage and spatial resolution of UTs at different positions, improving the system throughput. We establish the network channel model and design precoding vectors to maximize the system sum rate. We provide an iterative design of the precoding vectors in general case and propose an asymptotically optimal beam division multiple access (BDMA) transmission scheme with a large number of fiber ports. The simulation results demonstrate that our proposed system can achieve tens of Gbps per UT and several Tbps in system throughput. Finally, we construct an experimental system capable of achieving 10 Gbps transmission rate of each link and real-time wireless transmission of 4K video streams.

In recent years, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. Unfortunately, the high mobility, as well as perturbation, of UAV poses a great challenge in aligning narrow high-gain beams between the UAV and base station (BS). To tackle this challenging issue, we propose efficient beam prediction and tracking solutions from the perspective of control in this paper. First of all, for an important and typical flight mode in practice (i.e., the mission flight mode - to assign a series of targets in advance and fly from one target to the next one in turn), we study in depth the underlying control principle and reveal important properties and relationships between beam direction and controlled variables. Then, to exploit the properties and relationships revealed, we propose an efficient learning-based beam prediction and tracking solution. Specifically, we develop an efficient learning model, together with offline training and online inference algorithms. To further reduce the computational complexity, we distinguish two kinds of beam offsets and prove an important property of the mission flight mode, i.e., a multicopter almost keeps fixed attitude and velocity in most part of a flight process, based on which an efficient algorithm is designed. Comprehensive experiment results from open-source software, hardware and real UAV confirm the effectiveness of our control-assisted approach.

Because of the flexible scheduling, improved reliability, enhanced capacity over much wider range, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. However, the high mobility and perturbation of UAV impose a challenge on aligning narrow beams between the UAV and another node, such as the base station (BS). Although the position and attitude of UAV have been exploited to develop beam tracking algorithms, they belong to current or past information, which often provide limited performance improvement in the high-mobility scenario. To tackle this challenging issue, we, for the first time, identify a kind of important but ready-made information - the command or control sequence (CCS) provided by the flight control system (FCS). We explain in detail that CCS provides real and direct (rather than estimated) future information for beam prediction. Then, we propose an efficient learning-based algorithm to exploit the information. In particular, we prove theoretically that the convolutional neural network (CNN) is an appropriate choice of the network structure within the nonlinear prediction model. Experiment results from practical real UAVs confirm the effectiveness and superiority of our proposal.

The sixth-generation (6G) wireless network will bring major advances in ubiquitous connectivity, support massive device deployments, and open up new opportunities for diverse and decentralized Internet of things (IoT) applications. However, it also raises inherent trust issues, as IoT devices controlled by various independent entities cannot fully trust each other, thereby complicating secure and efficient random access control. To address this challenge, existing research turns to blockchain technology as a promising secure and trusted solution. This paper aims to explore and analyze blockchain-enhanced random access management from a game-theoretic perspective. We first investigate the well-known rogue’s dilemma during random access process, where selfish devices exploit access protocols and reduce system fairness. Next, we construct a two-device mixed-strategy game to illustrate the strategic interaction between honest and selfish IoT devices. To capture realistic behaviors under bounded rationality, we extend the analysis to a multi-device scenario with evolutionary game theory and replicator dynamics. Numerical results show that unregulated selfish behavior severely reduces IoT network efficiency. In contrast, blockchain-based penalties effectively discourage malicious actions and promote stable and efficient access equilibria.

Hybrid digital-analog precoding is a pivotal transmission technique to balance communication performance and hardware costs associated with radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO). However, most existing designs utilize Shannon rate and assume an infinite blocklength, which is impractical for emerging finite blocklength (FBL) applications, such as massive machine-type communications. To fill in this gap, this paper investigates hybrid precoding optimization in the FBL regime. The aim is to maximize the weighted sumrate (WSR), while fulfilling the transmit power budget at the base station (BS) and users' minimum rate requirements. The formulated optimization problem is highly challenging to solve, particularly due to the complex and nonconcave FBL rate function and the intricate coupling between analog and digital precoders. To tackle these issues, we propose a computationally efficient solution based on the penalty dual decomposition (PDD) method, which is guaranteed to converge to the Karush-KuhnTucker (KKT) solutions under mild conditions. Simulation results demonstrate that our proposed hybrid precoding design significantly outperforms several baseline schemes, especially those ignoring the impact of blocklength and adopting Shannon rate as the performance metric.

Multiple-input-multiple-output (MIMO) optical wireless communication (OWC) is a promising technology capable of providing high data rates. However, interuser interference can negatively impact system performance. Existing studies employ CVX-based beamforming designs to mitigate this issue, but these approaches suffer from high computational complexity. To tackle this challenge, this article studies the low-complexity beamforming design to maximize the sum rate. Specifically, we first utilize the fractional programming (FP) method to decompose the original beamforming problem into a series of convex subproblems and propose a block coordinate descent (BCD)-based beamforming framework. Then, we provide closed-form or semi-closed-form solutions for these subproblems. For the general approximate sum rate maximization, we drive the optimal semi-closed-form beamforming structure based on the Karush-Kuhn–Tucker (KKT) conditions. Furthermore, we propose a closed-form solution for the upper bound maximization by taking advantage of the separability of optical power constraints. Finally, numerical results indicate that the proposed beamforming designs reduce the computational complexity significantly while keeping the sum rate performance.

Continuous authentication (CA) has been widely applied by network service providers to verify user identities in finance, healthcare, and e-commerce fields. However, in next-generation networks, CA faces the risk of user privacy leakage due to its dependence on a verifier-centric authentication model, and verifiers may not always be trustworthy, particularly in zero-trust architecture networks. Existing privacy protection schemes face challenges in solving this problem because these schemes will weaken the linkability of context requests, leading to difficulties in consistency checks for fine-grained CA. To fill the gap, this article proposes a privacy-preserving CA (PPCA) scheme by incorporating anonymous self-sovereign identity and fine-grained CA. Specifically, PPCA exploits subset proof to enable users to reveal only the minimum necessary identity data for selective disclosure. To support fine-grained CA, we construct a new consistency proof for the anonymous user to prove that the different credentials are bound to the same attributes set, where the user is responsible for deciding whether to send the consistency proof. PPCA is formalized, defined, and constructed based on BLS signatures, Set Commitment, and Sigma Protocol. The security analysis shows that PPCA is correct and sound and supports user anonymity and credential consistency at the same time. The performance evaluation shows that PPCA requires only minimal additional time cost, achieving an optimal balance between security and efficiency.

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications; however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results.

The rapid evolution of blockchain has established it as a critical enabler for decentralized zero-trust services and networks. Without relying on traditional trust mechanisms such as pre-established mutual trust or central authentication, blockchain facilitates trust-free services via smart contract. Smart contracts offer verifiable software trust for various blockchain-enabled services (BESs) while protecting participants’ interests. However, the impact of blockchain on BES remains underexplored and unclear. In this work, we consider a general BES framework suitable for diverse decentralized zero-trust services and assess the role of blockchain in BES. We first build anM/G/1-type queuing model for BES and establish the stability conditions using matrix analytic methods. Based on the stability conditions, we identify the blockchain scalability and server capability as two critical bottlenecks of BES. We further use a tandem queuing model to describe the BES latency of the assembling and service phases. We analytically characterize the properties such as the convexity of service-phase latency with respect to traffic intensity, and highlight the BES pooling effects from traffic offloading and resource sharing. At last, we verify our conclusions through simulations and explore potential pathways for more efficient BES frameworks.

Mobile edge computing (MEC) has emerged as a disruptive paradigm that facilitates effective offloading from clouds and enables processing tasks near users. With the surge of mobile data traffic and escalating demands for responsive wireless services, it becomes imperative to enhance trust, security, and efficiency within MEC environments. Blockchain technology, renowned for its immutability, transparency, and security, has proven to be a compelling solution for securing data, enhancing supervision, and fostering trusted collaborations among heterogeneous MEC stakeholders. Despite these advantages, integrating blockchain with MEC also introduces a substantial efficiency bottleneck. Specifically, the blockchain consensus process can result in the aging of critical MEC system information, causing users to perform suboptimal service decisions, thus risking a degradation in service performance. To analyze this bottleneck, we first explore a blockchain-based MEC model that ensures secure task processing across diverse stakeholders. We employ the practical Byzantine fault tolerance (PBFT) consensus to validate and share service statuses, providing critical on-chain references for users to select their preferred target MEC servers. We then introduce the age-of-information (AoI) as a metric of freshness to characterize the aging of status reports, identifying variable consensus delay as a key factor affecting AoI. We reveal the critical impact of AoI on MEC service performance through analysis, challenging the notion that a lower AoI always leads to better performance. Finally, we validate our analysis and findings through comprehensive simulations.

Blockchain radio access network (B-RAN) offers a promising solution for trustworthy wireless applications by leveraging blockchain and smart contracts. However, the existing literature falls short in addressing the corresponding modeling and theoretical analysis. In this study, we develop analytical models to characterize the wireless access process in B-RAN, which also sheds light on other blockchain-enabled services. We first construct a two-dimensional queuing model and identify both blockchain scalability and RAN capability as two system bottlenecks, and then introduce the matrix analytic method to reduce complexity and improve efficiency. Furthermore, we build tandem queuing models for obtaining tight latency bounds with closed-form expressions. The performance and complexity of the proposed models are evaluated and compared against existing benchmarks to provide a comprehensive perspective. Finally, we present experimental results from a lab-built B-RAN prototype to demonstrate the efficacy of our models.

Non-orthogonal multiple access (NOMA) is recognized as a pivotal technology for enhancing spectral efficiency and facilitating massive connectivity in wireless communications. In practice, users are often divided into groups that occupy different resource blocks, while the users in the same group share the same resource through power-domain multiplexing, leading to multi-group NOMA (MG-NOMA). Hence, the full benefit of MG-NOMA relies on optimum power allocation, which, unfortunately, leads to difficult optimization problems that have not been well solved so far. This work investigates the optimal power allocation to achieve the maximum spectral efficiency and energy efficiency with quality-of-service (QoS) requirements and arbitrary user weights in MG-NOMA. We show that the complicated MG-NOMA power allocation problems can be decomposed into two layers, the lower layer for power allocation within each group and the upper layer for the power budget allocation among groups. More importantly, we reveal that, although quite difficult, the two-layer problems both contain the hidden convexity, indicating the achievability of the globally optimal solution. Then, we derive the optimal power allocation in either closed or semi-closed form for the maximum spectral efficiency and energy efficiency in MG-NOMA, respectively. The simulation results demonstrate the superiority of the proposed optimal power allocation schemes.

In this paper, we investigate precoder design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first reveal the effect of the precoder on the effective channel at receivers and formulate the precoder design for a group of subcarriers as a sum-rate maximization problem, where the delay spread of the effective channel is constrained to maintain its smoothness. Then with the beam based channel model and beam domain channel sparsity, the design of space domain precoders for a group of subcarriers are transformed into that of a space-frequency (SF) beam domain vector and the resulting space domain precoder at each subcarrier is beam structured. Efficient calculation for design and implementation of the beam structured precoder (BSP) is proposed. Moreover, effective channel estimation with the BSP is discussed. Simulation results show that the proposed BSP can enhance the effective channel estimation performance and significantly improve the system performance.

This paper presents a new multiple-input multiple-output (MIMO) communication system. The system, termed as “pseudo MIMO”, facilitates the transmission of more parallel data streams than the number of receiving radio frequency (RF) chains. The key principle involves connecting multiple antenna elements to each receiving RF chain and employing multiple analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling period. Through detailing the entire transmission and signal processing procedures by matrix manipulation, the equivalent channel matrix for each OFDM subcarrier is derived in a unified matrix form, which can be further represented by the product of the analog combining matrix and the frequency domain wireless channel matrix. As per the equivalent channel matrix, an optimization problem is formulated and solved to maximize the spectral efficiency by jointly designing the digital precoding and analog combining matrices. Numerical results demonstrate that the performance of a pseudo MIMO system can closely approach that of a fully digital (FD) MIMO system with the same antenna configuration but more RF chains. It is also revealed that a low-precision discrete analog combining scheme is sufficient to achieve nearly optimal performance. Further, the effectiveness of pseudo MIMO technology is validated through prototype testing.

Hybrid digital-analog precoding is essential for balancing communication performance, energy efficiency, and hardware costs in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most existing designs rely on the Shannon capacity and assume infinite blocklengths, which are impractical for emerging applications, such as massive machine-type communications, operating with finite blocklength (FBL). To address this gap, this paper pioneers a novel hybrid precoding design for mmWave massive MIMO in the FBL regime. We meticulously optimize hybrid precoding based on both the weighted sum-rate (WSR) and the max-min fairness (MMF) criteria, while fulfilling the transmit power budget and users’ minimum rate requirements. Both continuous and discrete phase shifters are considered for analog precoding. The formulated optimization problems are highly challenging to solve due to the nonconvex objective functions and nonconvex constraints. These challenges are further intensified by the nonconcave FBL rate function and the intricate coupling between analog and digital precoders. By proposing novel problem transformation and decomposition techniques, we reformulate the original complex problems into forms solvable with the penalty dual decomposition (PDD) method. We then develop two efficient iterative algorithms with parallel, and even closed-form variable updates, and guaranteed convergence to solve the WSR and MMF optimization problems, applicable to both continuous and discrete phase shifters. Simulation results show that our proposed hybrid precoding designs significantly outperform several baseline schemes, especially those adopting the Shannon capacity and infinite blocklength. Additionally, our proposed optimization algorithms enable hybrid precoding exploiting discrete phase shifters with limited quantization resolution (e.g., 3-bit) to closely match the performance of fully digital precoding in FBL scenarios.

Blockchain-based decentralized applications (DApps) offer enhanced security and decentralization features; nevertheless, their maintenance demands substantial computational resources and poses challenges for deployment in mobile networks. To address this, a number of studies have explored offloading blockchain mining tasks from mobile users to mobile edge computing (MEC) servers. However, the existing literature overlooks the fact that MEC servers can not only mine for mobile users but also mine for themselves, potentially explaining why MEC mining offloading has not gained broad acceptance within the industry. In this work, we exploit a more practical case and rethink the question of whether MEC servers lease computing power to mobile users by taking into account that MEC servers can mine for themselves. We establish a game model and apply backward induction to analytically characterize Nash equilibria for mining strategies adopted by MEC and mobile users. Our findings suggest that, if MEC can mine for self, MEC would mine for mobile users only under specific conditions where mobile users possess superior information gathering capability (at least better than the MEC server) or the whole blockchain system exhibits significant network value. We further provide a series of simulations to verify our conclusion and illustrate the impact of network parameters on the strategies of both sides.

Non-orthogonal multiple access (NOMA) has been regarded as a promising technology for next-generation wireless networks. Most of the existing NOMA schemes are designed with the assumption of infinite blocklength (IBL), which may lead to suboptimal performance in practice. This paper investigates the reliability-oriented downlink multiple-input multiple-output (MIMO)-NOMA design with finite blocklength (FBL) transmission. Specifically, we formulate the resource allocation problem to minimize the average block error rate (BLER) of FBL-MIMO-NOMA systems. We obtain the globally optimal solutions to the problem by proposing an efficient power, rate, and blocklength allocation scheme. The superiority of our proposed design is demonstrated via simulation results.

The use of log files in digital forensics highlights the importance of ensuring their data integrity for auditing purposes. However, traditional centralized audit log systems face challenges in maintaining data integrity due to log injection attacks and single-point failures. Although blockchain technology can accurately process and replicate log files, existing blockchain-based audit log systems still suffer from security and reliability issues due to their weak threat models and limited scalability. To address these concerns, we propose a blockchain-based audit log system that ensures data integrity under a general threat model where a part of the nodes, including loggers and auditors, are untrusted. First, our proposed system resists collusion attacks by incorporating multiple nodes for system processes and utilizing smart contracts to enforce consensus algorithms. Second, to save blockchain storage space, we design an efficient log integrity proof method, which generates a sub-Non-Fungible Token (sub-NFT) for each log file and keeps it on the blockchain as integrity proof. The single-point failure problem is resolved by outsourcing log files to a distributed file system. To evaluate the proposed system, we implement a prototype based on Hyperledger Fabric. Experimental results show that our proof generation method can reduce storage space usage in comparison to other blockchain-based audit log systems, saving approximately 50% of space in Hyperledger Fabric. The security analysis proves that our system can ensure log file data integrity under the proposed threat model.

As a promising technology for future networks, non-orthogonal multiple access (NOMA) has drawn great attention for its enhanced throughput with massive connectivity. Most of the existing NOMA schemes employ the ideal information rate as the performance metric, assuming perfect successive interference cancellation (SIC), which may lead to suboptimal performance in practice. In this paper, by considering the imperfect SIC and practical modulation schemes, we propose a power control scheme for uplink NOMA systems with the aim of maximizing the effective throughput. To address this problem, we first analyze the symbol error probabilities of two users employing the quadrature amplitude modulation (QAM) scheme, and then a specific expression of the effective system throughput is provided, which considers both the error performance and the data rate. Considering the complicated effective throughput expression of the uplink NOMA design, we next derive a lower bound of the effective throughput and consequently obtain an efficient power control scheme in closed form by maximizing the lower bound of the effective throughput. Numerical results are provided to demonstrate the superiority of our proposed scheme.

Cell-free (CF) networks are a promising architecture poised to revolutionize future wireless communication systems. To enhance performance, designing effective transmission strategies for CF networks is of practical significance. In this paper, we study the downlink beamforming design to maximize the resource efficiency (RE) of CF networks, which encompasses both energy efficiency (EE) and spectral efficiency (SE) optimization, thereby enabling the realization of an EE-SE tradeoff. Specifically, we formulate a RE maximization problem taking into account both the quality of service (QoS) requirements of the users and the power constraint of the network. The RE optimization problem is a non-convex fractional program. To solve it efficiently, we first equivalently decompose the challenging RE problem into two more tractable problems, a subproblem and a primary problem. Then, we show that the subproblem is equivalent to a power minimization problem and propose two effective methods to obtain the optimal primal and dual solutions simultaneously. After that, we derive the gradient of the optimal value function of the subproblem exploiting the obtained primal and dual solutions that facilitates the design of efficient algorithms with rapid convergence to address the primary problem. Finally, numerical results demonstrate that the proposed algorithms can effectively balance the EE-SE tradeoff, surpassing existing approaches in terms of either EE or SE.

The advent of blockchain technology in wireless networking has spawned a novel decentralized paradigm that engenders the establishment of multi-party trust, secure and efficient resource sharing, and equitable distribution of benefits. However, blockchain often results in lengthy access latencies and can hardly guarantee the quality of off-chain services. The problems stem from the intrinsic flaws of blockchain in dealing with off-chain services such as wireless access. In this study, we propose a unified framework that guarantees service quality and low establishment latency for blockchain-based systems, and incentivizes clients and service providers (SPs) to cooperate without a trusted third party. We use wireless access service in blockchain radio access network (B-RAN) as a typical example and provide a detailed implementation design. We model the service delivery process in our framework and identify the necessary condition for achieving trusting cooperation. Interestingly, we find the necessary condition is also sufficient for cooperation if the service framework is properly designed. Furthermore, we uncover a trade-off between system robustness and transmission efficiency, which offers insightful guidance for framework design in practice. Finally, we present simulation results to illustrate the effectiveness of our proposed wireless access scheme.

In this paper, we investigate signal detection for HF skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce beam based channel models (BBCM) in the space domain at each subcarrier and in the space-frequency domain for all subcarriers. Based on the BBCM in the space domain, we propose a beam structured detector (BSD) for each subcarrier. Specifically, we prove that the space domain detector design can be transformed into that of a beam domain detector without sacrificing optimality, and the asymptotically optimal space domain detector is beam structured with a low-dimensional beam domain detector, thus significantly reducing the design and implementation complexities. Furthermore, we extend the BSD to the space-frequency domain based on the BBCM jointly for all subcarriers. The design of space-frequency domain detector is also converted to that of a low-dimensional beam domain detector, which enables a very efficient design and implementation of BSD. Simulation results demonstrate the low complexity and satisfactory performance of the proposed detectors.

In this paper, we investigate high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. Based on the triple-beam (TB) based channel model and the channel sparsity in the TB domain, we propose a beam structured channel estimation (BSCE) approach. Specifically, we show that the space-frequency-time (SFT) domain estimator design for each TB domain channel element can be transformed into that of a low-dimensional TB domain estimator and the resulting SFT domain estimator is beam structured. We also present a method to select the TBs used for BSCE. Then we generalize the proposed BSCE by introducing window functions and a turbo principle to achieve a superior trade-off between complexity and performance. Furthermore, we present a low-complexity design and implementation of BSCE by exploiting the characteristics of the TB matrix. Simulation results validate the proposed theory and methods.

Massive multiple-input multiple-output (MIMO) multicasting is a promising approach for simultaneously delivering common messages to multiple users in next-generation wireless networks. However, existing studies have exclusively focused on multicast beamforming designs based on the Shannon capacity, assuming the infinite blocklength (IBL) for transmission. This assumption may lead to strictly suboptimal designs for practical multicast transmissions with finite blocklength (FBL), especially in ultra-reliable low-latency communications. In this paper, we explore the beamforming design for massive MIMO multi-group multicasting in the FBL regime. Our study considers both the max-min fairness and the weighted sum rate criteria for a comprehensive treatment. Due to the non-concave FBL rate function, the resulting optimization problems are known to be notoriously hard. We characterize the necessary and sufficient condition for the non-negative FBL rate to be a concave function of the received signal-to-interference-plus-noise ratio (SINR). Considering a finite number of transmit antennas, we propose low-complexity majorization-minimization (MM) type algorithms, which update variables in either closed or semi-closed form, to achieve locally optimal solutions of the formulated optimization problems. We further show that, as the number of transmit antennas becomes large, the optimal beamformer of each group aligns asymptotically with a linear combination of the channel vectors of that group of users, where the optimal normalized combining coefficients are derived in closed form. Subsequently, we obtain the globally optimal multicast beamformers by optimizing the power allocation using low-complexity iterative algorithms. Simulation results show that the proposed schemes outperform several existing methods, especially those employing the Shannon capacity as the performance metric. Moreover, the proposed algorithms exhibit complexities that only slightly grow with the number of transmit antennas and they can notably reduce the computation time by up to two orders of magnitude over the benchmarks, making them highly beneficial for massive MIMO applications.

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for enabling massive connectivity and achieving high spectral efficiency in future wireless communication. In the literature, most of the existing NOMA schemes are designed with the assumption of infinite blocklength, which may lead to suboptimal performance in practical communication systems. Thus, this paper investigates the downlink NOMA system with finite blocklength (FBL) transmission, namely the downlink FBL-NOMA system. We focus on developing a joint resource allocation scheme from single-channel to multi-channel systems, aiming at minimizing the average block error rate (BLER) for the downlink FBL-NOMA. Specifically, for single-channel systems, the optimal rate and power allocation scheme are derived in semi-closed form. For multi-channel systems, we conduct the convexity analysis of the minimum BLER problem and provide the optimal solution in waterfilling form for convex cases, as well as an efficient majorization-minimization (MM)-based algorithm for general cases. We also propose an efficient method to jointly optimize channel assignment, rate allocation, and power allocation for multi-channel FBL-NOMA systems. Simulation results demonstrate the superiority of the proposed designs over the existing NOMA and orthogonal multiple access (OMA) schemes in terms of reliability and efficiency.

The current multiple-input multiple-output (MIMO) systems are still mainly implemented based on the centralized architecture, which thus has to process a huge amount of base-band data. In particular, the central processing unit (CPU) needs a large bus bandwidth to accommodate the prohibitive baseband data transmission, which hinders effective system implementation, especially for the massive MIMO systems. Moreover, the centralized scheme lacks flexibility and scalability when facing varying sizes of antenna arrays and diverse applications. This paper proposes an efficient decentralized bidirectional-chain (DBC) equalizer architecture. The advantages of the DBC architecture are two-fold. First, it can reduce the data traffic transmitted from the antennas to the processing unit by categorizing them into clusters, each of which is equipped with a local processing unit (LPU). Second, it can reduce the time delay by updating all clusters in parallel. To sufficiently exploit the proposed DBC architecture, we further propose efficient parallel iterative algorithms. The DBC-based parallelizable iterative algorithms achieve the state-of-the-art performance in terms of convergence rate and bit error rate. Finally, simulation results are provided to confirm the effectiveness and superiority of our proposal.

In this paper, we address the peak-to-average power ratio (PAPR) reduction problem of an orthogonal frequency division multiplexing (OFDM) system under error vector magnitude (EVM) and out-of-band emissions (OOBE) constraints. Unlike the existing methods, we first equivalently transform the non-convex objective function—PAPR into the difference between the numerator and denominator, and then analyze its monotonicity of the auxiliary variable. Subsequently, we propose a novel algorithm based on bisection searching to tackle the equivalent PAPR optimization problem. Furthermore, an efficient PAPR reduction algorithm is developed to overcome the challenge of high computational complexity, and a simplified version of this algorithm is provided. Finally, the performance of the proposed algorithms is compared with the state-of-the-art schemes. The simulation results demonstrate the superiority of the proposed algorithms.

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

In this paper, we investigate signal detection for HF skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce beam based channel model (BBCM) in the space domain and reveal the sparsity of the channel in the space-beam domain. Based on the BBCM in the space domain, we propose a beam structured detector (BSD) for each subcarrier. Specifically, we prove that the space domain detector design can be transformed to that of a beam domain detector without sacrificing optimality, and the asymptotically optimal space domain detector is beam structured with a low-dimensional beam domain detector, thus significantly reducing the design and implementation complexities. Furthermore, we provide a beam selection criterion to choose the beams that are used for the BSD. Simulation results demonstrate the low complexity and satisfactory performance of the proposed detector.

Recently, blockchain radio access network (B-RAN) arises as an innovative paradigm for the sixth-generation (6G) wireless communications to build cooperative trust, aggregate wireless resources, and schedule inter- and intra-network tasks among independent network entities. It establishes an open platform based on blockchain to provide diverse wireless services and applications, such as radio access, Internet of Things (IoT), and mobile-edge computing, via trusted interactions with enhanced security and efficiency. As a distinctive feature, B-RAN enables secure and efficient resource sharing and trading by aggregating, pooling, and coordinating resources from multiple resource hosts and owners across subnetworks. Therefore, an implementable architecture along with various functional modules shall be delicately designed. This work aims to establish a unified architecture with enhanced efficiency, security, compatibility, and flexibility for resource sharing and trading in B-RAN. Specifically, we develop a six-layer architecture that incorporates a number of novel features, such as enhanced blockchain structures, secure interaction methods, efficient service mechanisms, and scalable transaction patterns. We design a number of pluggable functional modules in each layer to support diverse functions, services, and applications of resource sharing and trading. Finally, we implement a practical prototype based on the layered architecture for resource-limited devices. Multiple experiments are presented to verify the performance of the proposed architecture from different aspects.

Rate-splitting multiple access (RSMA) is a promising multiple access (MA) technique. It employs rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receiver. Most of the existing works on RSMA assume that all users use SIC to decode the common stream and the blocklength is infinite. The first assumption causes the data rate of the common stream to be limited by the user with the worst channel quality. The second assumption may lead to suboptimal performance in practical systems with finite blocklength. In this paper, we propose a flexible RSMA scheme, which allows the system to decide whether a user should use SIC to decode the common stream or not. We consider the effective throughput as the performance metric, which incorporates the data rate as well as the error performance of RSMA with finite blocklength. We first derive the effective throughput expression and then formulate an effective throughput maximization problem by jointly optimizing the beamforming vectors, transmission data rates, and RS-user selection. We develop an optimal algorithm as well as a low-complexity algorithm for beamforming design. We derive a semi-closed-form solution of the optimal data rates and propose an efficient algorithm for the RS-user selection. Numerical results demonstrate that the proposed algorithm obtains a higher effective throughput than space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and two other RSMA schemes.

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

In this paper, rapidly converging low-complexity iterative transmit precoding (TPC) techniques are proposed for the massive multiple-input multiple-output (MIMO) downlink. First of all, the proposed random block-based iterative TPC (RBI-TPC) algorithm performs its iterations by updating multiple rather than a single component at each instant, where the updating order of each block containing multiple components relies on the samples randomly sampled from a discrete distribution. Based on the analytically derived convergence rate, we demonstrate that improved convergence is achieved by the block-based update mechanism conceived since the correlation between multiple components can be beneficially exploited. Then, the random sampling that determines the updating order is studied. By applying conditional random sampling, the updating order is optimized based on the latest updates for attaining more rapid convergence. We also demonstrate that the associated updating order may become deterministic under specific conditions so that a fixed but optimized updating order can be used for facilitating the practical implementations, which paves the way for conceiving the ordered block-based iterative TPC (OBI-TPC) algorithm. Finally, the concept of successive over-relaxation (SOR) is adopted for further convergence improvement and simulations are presented to illustrate the performance improvements of the proposed RBI and OBI TPC algorithms compared to the existing low-complexity iterative TPC schemes.

Blockchain radio access network (B-RAN) is a decentralized, trustworthy wireless networking paradigm spurred by distributed ledger technologies (DLTs). In B-RAN, even though the blockchain builds trust in upper layers, the absence of trust between client devices still causes the problem with open access, or the so-called Rogue’s dilemma, and degrades the network performance. Therefore, Hash Access was proposed for B-RAN to address the trust issue between clients and enforce client devices to obey the grant-free access rule. However, the characteristics and performance of Hash Access in B-RAN remain unclear. In this work, we dive deep into the Rogue’s dilemma from a game-theoretic model to emphasize the necessity of Hash Access. We establish an analytical model to comprehensively evaluate the performance of B-RAN using Hash Access regarding transmission success probability, access delay, and network throughput. Based on the analytical model, we further optimize the Hash Access protocol for network throughput and provide useful practical guidelines. Simulation results are presented to validate our proposed model and insights.

The intelligent reflecting surface (IRS) technology is emerged as an enabling technology for beyond fifth-generation systems and Internet of Things networks in which the signal propagation is reconfigured to enhance wireless system performance. IRS consists of many passive elements and each reflecting the incident signal with a certain phase shift to collectively achieve the required beamforming. The IRS is to be a low profile and lightweight setting with a conformal geometry; hence, its position can be easily engineered to achieve certain performance enhancements. In the current literature, however, the flexibility in the IRS position is often overlooked since it is considered as a given fixture. We argue that optimizing the IRS position provides a new degree of freedom in the network design and enables extra performance gain. In this article, we analytically characterize the optimal IRS’s position to maximize the achievable system rate. We then obtain the optimal IRS positions for different IRS settings with fixed height and variable height and consider both cost-efficient equal phase shift IRS, and nonequal phase shift IRS that enables sophisticated beamforming. We further incorporate antenna directivity in our analysis and investigate its effect on the optimal IRS position in each case. Simulation results show that the provided optimal position yields higher performance than settings with random IRS locations. Our results provide significant practical insights on the network coverage design using the IRS.

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely theeffective throughput, which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.

This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

We investigate the downlink (DL) transmit strategy for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication (SATCOM) systems, in which only the slow-varying statistical channel state information is known at the transmitter side. First, we derive the massive MIMO LEO satellite channel model, when the uniform planar arrays are deployed at both the satellite and user terminals (UTs). Building on the rank-one property of the satellite channel matrices, we show that transmitting a single data stream to each UT is optimal in the sense that the ergodic sum rate is maximized. This result is of great importance for massive MIMO LEO SATCOM systems, since the sophisticated design of transmit covariance matrices is turned into that of precoding vectors, without loss of optimality. Furthermore, we develop an algorithm to compute the precoding vectors. Simulation results show the significant performance gains of the proposed approaches over the existing schemes.

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.

Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory,Theorem 1presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically,Theorem 1fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applyingTheorem 1to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure inTheorem 1and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, trustworthy radio access paradigm spurred by blockchain technologies. However, many characteristics of B-RAN remain unclear and hard to characterize. In this study, we develop an analytical framework to model B-RAN and provide some basic fundamental analysis. Starting from block generation, we establish a queuing model based on a time-homogeneous Markov chain. From the queuing model, we evaluate the performance of B-RAN with respect to latency and security considerations. We uncover a more comprehensive picture of the achievable performance of B-RAN by connecting latency and security. At last, we present experimental results via an innovative prototype and validate the proposed model.

This work proposes a fiber-enabled optical wireless communication (FE-OWC) system for bidirectional communications between the base station (BS) and a number of mobile user terminals (UTs) via full beam coverage, aimed at facilitating ultra-high data rate communications. The FE-OWC system comprises optical antennas, optical chains, and baseband units at both BS and UTs. The innovative optical antenna consists of an array of fiber ports and a transceiver lens, which can form a number of transmit and receive optical beams and provide a full beam coverage for simultaneous downlink and uplink connections with a number of UTs, respectively. We present analysis to characterize downlink and uplink channel models and gains, including both optical and electrical parts, between the BS and UTs and conduct a complete link budget analysis. We further design downlink and uplink multiuser multiple-input multiple-output (MIMO) as well as massive MIMO transmission protocols and develop asymptotically optimal schemes for a large number of fiber ports. Numerical results illustrate that the FE-OWC system has the potential to support over 10 Gbps data rate per UT and Tbps system throughput required in future 6G mobile communication systems.

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users' channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

In this article, we investigate multi-cell scheduling for massive multiple-input-multiple-output (MIMO) communications with only statistical channel state information (CSI). The objective of multi-cell scheduling is to activate a subset of users so as to maximize the ergodic sum rate subject to per-cell total transmit power constraint. By adopting beam division multiple access based on the statistical CSI, i.e., channel-coupling matrix (CCM), we simplify multi-cell scheduling as a power control problem in the beam domain, by which the ergodic sum rate is maximized. To reduce the computational burden on finding the ergodic sum rate, we propose a learning-to-compute strategy, which directly computes the complex ergodic rate function from CCMs via a deep neural network. Specifically, by modeling the probability density function of the ordered eigenvalues of the Hermitian CCM matrices as exponential family distributions, a properly designed hybrid neural network makes the ergodic rate computation feasible. With the learning-to-compute strategy, the online computational complexity of multi-cell scheduling is substantially reduced compared with the existing Monte Carlo or deterministic equivalent (DE) based methods while maintaining nearly the same performance.

Non-orthogonal multiple access (NOMA) enables massive connectivity and achieves high spectral efficiency. The vast majority of the NOMA literature has adopted the ideal information rate as performance metric assuming perfect successive interference cancellation (SIC) without any error propagation, which, however, may lead to NOMA designs adverse to SIC. In this paper, we take into account imperfect SIC and practical modulation schemes for power-domain NOMA design. To characterize the error propagation, we derive the bit error rates (BERs) of the users for arbitrary-order quadrature amplitude modulation (QAM) schemes. Then, we propose a minimum error probability NOMA (MEP-NOMA) design, minimizing the average BER of the users via power allocation. Considering the complicated error probability expressions of the MEP-NOMA design, we derive lower and upper bounds on the average BER, based on which a simple closed-form power allocation is obtained. We show that the proposed power allocation minimizes both the lower and upper bounds on the average BER for a sufficiently large power budget and provides near-optimal error performance. On this basis, we theoretically prove the superiority of MEP-NOMA over existing OMA and NOMA schemes in terms of error performance. Comprehensive numerical results are provided to verify the accuracy of the error probability analysis of the considered practical NOMA scheme with imperfect SIC and to demonstrate the efficacy of the proposed MEP-NOMA design.

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data rates by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO downlink (DL) transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems by exploiting statistical channel state information (sCSI) at the transmitter. We first establish a massive MIMO channel model for LEO satellite communications and propose Doppler and time delay compensation techniques at user terminals (UTs). Then, we develop a closed-form low-complexity sCSI based DL precoder by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Motivated by the DL ASLNR upper bound, we further propose a space angle based user grouping algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems.

Various high-rate services in the fifth-generation (5G) mobile communication system will accelerate battery consumption. To minimize the power consumption at 5G devices, we propose an enhanced Discontinuous reception (DRX) mechanism with a new monitoring scheme, adjustable DRX cycles and adaptive sleep switching mode. Its performance is analyzed by modeling the system with a four-state semi-Markov process. Precise closed-form expressions of the relative power saving and wake-up delay are derived. Simulation results show the improvement of power saving and reveal a trade-off between relative power saving and wake-up delay of the enhanced mechanism.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

In recent years, network densification and edge caching become effective approaches to reduce the burden on the fronthaul links and the content delivery latency for wireless communication systems. However, maximizing system spectral efficiency cannot directly provide any insight on their energy requirements/efficiency for cache-enabled millimeter-wave (mmWave) radio access networks (RANs). In this paper, we study the design of energy-efficient transceiver, consisting of analog and digital precoder/combiner, for the delivery phase of the downlink of cache-enabled mmWave RANs. Due to the non-convexity of the delivery rate and objective, the coupling between the digital and analog precoders/combiners, and the constant module constraint on the elements of analog precoders/combiners, the problem of interest is non-convex and hard to obtain the global optimal solution, even the local optimal solution. To this end, we first overcome these challenges one-by-one and then transform the original problem into tractable one. Finally, an algorithmic framework that converges to the Karush-Kuhn-Tucker solution with provable is developed to achieve the design of energy-efficient transceiver. Numerical results are provided to evaluate the performance of the proposed algorithm, where fully digital precoding is used as benchmark.

The low cost and versatility of optical devices make it possible to pack a large number of optical transceivers into arrays and exploit massive multiple-input multiple-output (MIMO) transmission in optical wireless communications. Nevertheless, optical massive MIMO presents several distinct challenges such as, the line-of-sight propagation and intensity modulation, incompatible with existing radio frequency massive MIMO techniques. This paper presents a networked optical massive MIMO system that consists of multiple base stations (BSs), each equipped with a transmit lens and an optical transmitter array, cooperatively serving a number of user terminals (UTs), each equipped with a receive lens and a photodetector array. We establish the optical massive MIMO channel model, analyze its asymptotic behavior, and evaluate the potential of networked optical massive MIMO on system throughput improvement. To achieve high throughput, we propose optical beam division multiple access (BDMA) transmission schemes under the total and per transmitter power constraints with the asymptotic optimality. Our results show that the system sum rate increases proportionally to the number of BSs and UTs using the optical BDMA transmission. Further numerical results show that the proposed optical massive MIMO system along with the optical BDMA transmission is able to achieve high throughput with low complexity.

The use of the millimeter wave (mmwave) spectrum for next generation mobile communication systems has gained significant attention recently. Large antenna arrays along with beamforming techniques are required to combat the large path-loss at mmwave frequencies. However, the existing beam designs often cause a large peak to average power ratio, and thus require power-inefficient power amplifiers (PAs). In this paper, we propose power-efficient beam design methods that facilitate the use of power-efficient PAs. Specifically, we design digital and hybrid analog-digital mmwave beams that possess a per-antenna constant envelope (PACE) and thus are highly power-efficient. Meanwhile, we also minimize the ripples in the mainlobe and sidelobe of the beams and consider both infinite and finite resolution phase shifters. To this end, we first propose an efficient feasible point search method to provide a feasible solution for the considered difficult beam design problem. Then, a novel hybrid analog-digital mapping algorithm is developed to map a designed digital beam to a hybrid analog-digital beam. To achieve better performance, we propose an improved hybrid analog-digital beam design method employing further optimization based on the feasible point. The proposed method is applicable to both infinite-resolution and finite-resolution phase shifters. Comprehensive simulation results are provided to demonstrate the effectiveness and superiority of the proposed beam designs.

Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as promising technologies for the beyond fifth generation networks to achieve significant capacity improvement and delay reduction. In this paper, the technologies of hybrid NOMA and MEC are integrated. In the hybrid NOMA MEC system, multiple users are classified into different groups and each group is allocated a dedicated time slot. In each group, a user first offloads its task by sharing a time slot with another user, and then solely offloads during a time interval. To reduce the delay and save the energy consumption, we consider jointly optimizing the power and time allocation in each group as well as the user grouping. As the main contribution, the optimal power and time allocation is characterized in closed form. In addition, by incorporating the matching algorithm with the optimal power and time allocation, we propose a low complexity method to efficiently optimize user grouping. Simulation results demonstrate that the proposed resource allocation method in the hybrid NOMA MEC systems not only yields better performance than the conventional OMA scheme but also achieves quite close performance as global optimal solution.

Millimeter wave (mmwave) communication has attracted increasing attention owing to its abundant spectrum resource. The short wave-length of mmwave signals facilitates exploiting large antenna arrays to achieve large array gains and combat large path-loss. However, the use of large antenna arrays and narrow beams leads to a large overhead in beam training for obtaining channel state information, especially in dynamic environments. To reduce the overhead of beam training, in this paper we propose an environment sensing based beam training algorithm via deep reinforcement learning. The proposed algorithm can sense the change of the environment and learn required latent probability information from the environment, and intelligently trains beams with a low overhead. In addition, the proposed algorithm does not require any priori knowledge of dynamic channel modeling, and thus is applicable to a variety of complicated scenarios. Simulation results demonstrate the effectiveness and superiority of the proposed intelligent beam training algorithm.

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

In this paper, we consider a nonorthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system where the power and time are jointly optimized to reduce the energy consumption and delay. In order to achieve a tradeoff between energy consumption and delay, we introduce weighting factors, and the optimization problem is formulated to minimize the weighted sum of energy consumption and delay. In the literature, only two offloading strategies, i. e., orthogonal multiple access (OMA) and pure NOMA, are mainly considered. In this paper, we investigate a third strategy, hybrid NOMA, which contains the strategies of OMA and pure NOMA. As the main contribution, we analytically characterize the optimal resource allocation, i. e., the joint power and time allocation, for two-scheduled-user case. Simulation results show that the proposed resource allocation method in hybrid NOMA systems yields lower energy consumption and delay than the conventional OMA scheme.

The hardware implementation of large-scale multi-antenna systems requires power-efficient power amplifiers (PAs). However, the existing beamforming designs often cause a large peak-to-average power ratio and have to rely on power-inefficient PAs. In this paper, we propose a unified power-efficient beamforming design framework, which incorporates per-antenna constant envelope constraints to improve the power efficiency. We further propose an efficient algorithm named "sequential outer approximation procedure" (SOAP) to search a feasible point. Power-efficient design for beam pattern synthesis is developed based on SOAP.

We investigate physical layer massive multiple-input-multiple-output (MIMO) multicasting transmit design with statistical channel state information at the base station. We first establish the relationship between the transmit design problems under the quality of service and max-min fair criteria. Then we focus on the transmit designs under the latter criterion. We show that the eigenvectors of optimal input covariance are given by the columns of the discrete Fourier transform matrix for the uniform linear array, which reveals the optimality of beam domain transmission in massive MIMO multicasting. We further propose a dual algorithm together with stochastic programming to specify the eigenvalues of input covariance. In addition, a simplified input covariance optimization by applying the deterministic equivalent technique is presented to reduce the complexity involved in stochastic programming. Simulation results demonstrate the performance of the proposed algorithms.

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

Recently, edge caching and multicasting arise as two promising technologies to support high-data-rate and low-latency delivery in wireless communication networks. In this paper, we design three transmission schemes aiming to minimize the delivery latency for cache-enabled multigroup multicasting networks. In particular, full caching bulk transmission scheme is first designed as a performance benchmark for the ideal situation where the caching capability of each enhanced remote radio head (eRRH) is sufficient large to cache all files. For the practical situation where the caching capability of each eRRH is limited, we further design two transmission schemes, namely partial caching bulk transmission (PCBT) and partial caching pipelined transmission (PCPT) schemes. In the PCBT scheme, eRRHs first fetch the uncached requested files from the baseband unit (BBU) and then all requested files are simultaneously transmitted to the users. In the PCPT scheme, eRRHs first transmit the cached requested files while fetching the uncached requested files from the BBU. Then, the remaining cached requested files and fetched uncached requested files are simultaneously transmitted to the users. The design goal of the three transmission schemes is to minimize the delivery latency, subject to some practical constraints. Efficient algorithms are developed for the low-latency cloud-edge coordinated transmission strategies. Numerical results are provided to evaluate the performance of the proposed transmission schemes and show that the PCPT scheme outperforms the PCBT scheme in terms of the delivery latency criterion.

We investigate physical layer security in massive multiple-input multiple-output multicast transmission where the base station only knows the statistical channel state information of the legitimate user terminals and the eavesdropper. We first introduce a tight lower bound of the achievable secrecy multicast rate as the design objective. Then, we find the closed-form transmit directions, i.e., the eigenvectors of the optimal multicast transmit covariance matrix, which simplifies the matrix-valued multicast transmit strategy design into a beam domain power allocation problem. We further propose an efficient iterative power allocation algorithm with guaranteed convergence to a local optimal solution by invoking the concave-convex procedure. We also derive the deterministic equivalent of the optimization objective to reduce the computation complexity. Numerical results demonstrate the performance gains of the proposed approach over the conventional approach.

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

This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to$2K$and$K$under the total and per transmitter power constraints, respectively, where$K$is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam-domain optical massive MIMO communication approaches.

Millimeter wave (mmWave) communication emerges as an enabling technology for Gbps transmission. A further performance enhancement can be achieved by incorporating mmWave communication into fog radio access networks (F-RANs), which alleviate the large path loss of mmWave signals by shortening the distance between transmitters and users and reduce the latency by caching at enhanced remote radio heads (eRRHs). The full benefit of mmWave F-RANs is leveraged on a joint design of signal processing at the centralized baseband unit (BBU) and distributed eRRHs. In this paper, we propose an energy efficient hybrid precoding design for the downlink mmWave F- RANs with wireless fronthaul links, where digital and hybrid precoders are exploited at the BBU and eRRHs, respectively. We develop an effective method to solve the resulting difficult precoding optimization problem and provide numerical results to demonstrate the effectiveness of the proposed mmWave F-RAN design.

Millimeter wave (mmwave) communication operating in the band of 30-300 GHz is promising to provide Gbps data rates owing to its abundant spectrum resource, and has attracted increasing attention. Cooperative transmission, by converting undesired interferences into useful signals, is able to further improve performance of mmwave systems. In this paper, we propose a novel cooperative transmission scheme for mmwave communication networks, where each mobile user is cooperatively served by multiple access points (APs) that use hybrid precoders. Our goal is to maximize the system energy efficiency, which leverages on a joint design of the hybrid precoders of all APs. The formulated problem is a difficult nonlinear fractional programming subject to unit modulus constraints. We propose an efficient algorithm by incorporating penalty decomposition and block coordinate descent methods. Numerical results are provided to confirm the effectiveness of the proposed algorithm and reveal some important insights.

This paper considers an uplink multiuser multiple-input-multiple-output (MU-MIMO) system. In this system, we have presented a sparse-aware minimum-mean-square-error (SA-MMSE) detector which improves an underlying linear detector using the sparsity of a residual error vector (difference from the transmit vector and the detected one by the linear detector). Despite its attractive performance, the conventional SA-MMSE detector is only available for 4-QAM systems. In this paper, we generalize the SA-MMSE detector for a higher-order modulation system in a non-trivial method. This is referred to as generalized SA-MMSE (GSA-MMSE) detector. The key idea of the proposed detector is to exploit the hierarchical structure of a residual error vector. To be specific, the residual error vector can be decomposed into orthogonal sub-error vectors and, leveraging the orthogonality, the sub-error vectors can be decoded using the corresponding SA-MMSE detector in a successive fashion. Via simulation results, we demonstrate that the GSA-MMSE detector significantly outperforms the conventional linear detectors with a comparable complexity.

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

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

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

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

Non-orthogonal multiple access (NOMA) enables user multiplexing in the power domain via successive interference cancellation (SIC). The key to achieve the full benefit of NOMA is resource allocation, including power allocation and channel assignment for all users, which leads to difficult mixed integer programs. In the literature, the optimal power allocation has only been investigated for users on a single channel (or in one group), while the joint optimization of power allocation and channel assignment generally requires an exhaustive research. In this paper, we investigate resource allocation in downlink NOMA systems. We analytically characterize the optimal power allocation in closed-form for sum rate maximization with weights or quality of service (QoS) constraints. Furthermore, we also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal multichannel power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.

Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This paper focuses on distributed interference management for downlink small cell networks. We address the optimization of transmit strategies from both the game theoretical and the network utility maximization (NUM) perspectives and show that they can be unified in a generalized Nash equilibrium problem (GNEP) framework. Specifically, the small cell network design is first formulated as a GNEP, where the SBSs and MBS compete for the spectral resources by maximizing their own rates while satisfying global quality of service (QoS) constraints. We analyze the GNEP via variational inequality theory and propose distributed algorithms, which only require the broadcasting of some pricing information, to achieve a generalized Nash equilibrium (GNE). Then, we also consider a nonconvex NUM problem that aims to maximize the sum rate of all BSs subject to global QoS constraints. We establish the connection between the NUM problem and a penalized GNEP and show that its stationary solution can be obtained via a fixed point iteration of the GNE. We propose GNEP-based distributed algorithms that achieve a stationary solution of the NUM problem at the expense of additional signaling overhead and complexity. The convergence of the proposed algorithms is proved and guaranteed for properly chosen algorithm parameters. The proposed GNEP framework can scale from a QoS constrained game to an NUM design for small cell networks by trading off signaling overhead and complexity.

Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.

This letter investigates the optimal power allocation for weighted sum rate maximization (WSRM) in nonorthogonal multiple access (NOMA) systems with power order and quality-of-service (QoS) constraints. We show that the NOMA WSRM problem is a convex problem under some condition of the user weights. The optimal solution to the WSRM problem without QoS constraints is analytically characterized in two cases. Then, we investigate the feasibility of the WSRM problem with QoS constraints. We further show that the power order constraint can be omitted without loss of any optimality under some mild condition of the QoS thresholds, which enables us to derive an analytical expression of the optimal power allocation.

In this paper, we develop an optimal precoder and a suboptimal precoder for uplink distributed antenna systems with transmit correlation over Rician channels. The objective is to minimize a pairwise error probability (PEP) with constrained transmit power. The optimal scheme uses an efficient projection-based steepest descent algorithm to obtain the Gram matrix of the precoder, while the suboptimal scheme exploits the eigen-structure of the Gram matrix to obtain its unitary matrix (beamforming) and eigenvalues (power control). For a given beamforming (BF), an efficient power adaptation based on a fuzzy signal-to-noise ratio (SNR) is developed. The PEP analysis at low SNR is used to obtain a suboptimal BF. The derived BF and the fuzzy power adaptation have closed-form expressions that make the suboptimal scheme computationally efficient in comparison with existing methods. The BF can be re-optimized via the Riemannian steepest descent method to further reduce the PEP. Simulation results show that the proposed optimal scheme not only gives the performance the same as the existing optimal scheme when the latter can converge, but it also has lower computational complexity and faster convergence. Moreover, the suboptimal scheme can give a nearly optimal error rate.

Owing to abundant spectrum resources, millimeter wave (mmwave) communication promises to provide Gbps data rates, which, however, may be restricted by large path-loss. Thus, antenna arrays are commonly used along with beam alignment (BA) as an important step to achieve the array gain. Efficient BA relies on the beam training codebook design. In this paper, we propose a new hierarchical codebook to achieve uniform BA performance with low overhead. To better elaborate on the design principle, a single-path channel model is considered first to frame the proposal. The codebook design is formulated as an optimization problem, where the ripple in the main/side lobes is constrained such that each training beam is close to the ideal one with a flat magnitude response and a narrow transition band. Then, we propose an efficient algorithm to find such a beam training codebook. Furthermore, we derive closed-form expressions of the BA misalignment probability or error rate of the proposed beam training codebook. Our results reveal that using the proposed codebook, the error rate of tree-search-based BA exponentially decreases with the SNR for a given channel, and linearly decreases in the log-log coordinate axis for a fading channel. We further propose a power allocation scheme used in different training stages to further improve the BA performance. Finally, the proposed framework is extended to the more complex case of multi-path channels. Numerical results confirm the effectiveness of the proposed training codebook and power allocation scheme as well as the accuracy of the performance analysis.

With the rapid development of smart handled devices and mobile internet services, mobile network operators (MNOs) have experienced an explosive growth in traffic demand in cellular access networks. Intelligently offloading traffic through small-cell networks has been widely considered as an efficient approach for MNOs to relieve traffic congestion in cellular access networks and accommodate more mobile users (MUs) with satisfactory quality of service (QoS). However, offloading traffic to small-cell networks might incur co-channel interference among the MUs. Such interference, if without a proper control, will lead to significant power consumptions of the MUs, which undermines the benefit of traffic offloading. In this paper, we are motivated to investigate the joint access-selection and power allocation problem, in which the MUs are appropriately selected to offload their traffic demands to different small-cell networks with proper transmit-powers. Our objective is to maximize a system-reward that takes into account both the MNO's economic reward for serving the MUs and the MUs' transmit-power consumption costs. The formulated problem corresponds to a mixed binary and non-convex optimization problem. We exploit the decomposable structure of the problem and propose an efficient algorithm to solve it. Numerical results are provided to show the performance of the proposed algorithm as well as the benefits of the proposed traffic offloading scheme.

Wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented demand for higher data rates. Energy-efficient transmission has been regarded as a key technology for the next-generation wireless system. Meanwhile, to reduce the cost, in practice, a base station usually has less radio chains than the antennas, which makes antenna selection an appealing transmission strategy. This letter addresses the problem of joint optimization of energy-efficient beamforming and antenna selection for downlink multiuser systems. The nonconvexity arising from both the nonlinear fractional programming and the ℓ0-(quasi)norm presents the main difficulty in solving the joint optimization problem. Nevertheless, we develop an effective algorithm to address this problem. Numerical results are given to validate the effectiveness and the performance of the developed algorithm.

Device-to-device (D2D) communication underlaying cellular networks improves spectral efficiency but causes interference to cellular users (CUs). Such interference can be utilized to help CUs prevent wiretapping. This paper aims to achieve the twofold goal of security provisioning for CUs and spectral efficiency enhancement for D2D links by optimizing the resource sharing of CUs and D2D links. We first provide the necessary and sufficient conditions for the accessibility of a CU channel by a D2D link. Then, we derive the jointly optimal resource sharing strategy, including the closed-form power control and the optimal channel pairing of CUs and D2D links. Numerical results show that the proposed strategy can improve both the CUs' security and D2D spectral efficiency.

Visible light communication (VLC) combined with advanced illumination may be expected to become an integral part of next-generation heterogeneous networks. In order to mitigate the performance degradation imposed by the intercell-interference (ICI), a user-centric (UC) cluster formation technique employing vectored transmission (VT) is proposed for the VLC down-link system, where multiple users may be simultaneously supported by multiple access points (APs). In contrast to the traditional network-centric (NC) design, the UC-VT cluster formation is dynamically constructed and adjusted, rather than remaining static. Furthermore, we consider the critical issue of multiuser scheduling (MUS) relying on maximizing the “sum utility” of this system, which leads to a joint cluster formation and MUS problem. In order to find a practical solution, the original problem is reformulated as a maximum weighted matching (MWM) problem relying on a user-AP distance-based weight and then a low-complexity greedy algorithm is proposed, which offers a suboptimal yet compelling solution operating close to the optimal value found by the potentially excessive-complexity exhaustive search. Our simulation results demonstrate that the proposed greedy MUS algorithm combined with the UC-VT cluster formation is capable of providing an average user throughput of about 90% of the optimal throughput, which is about three times the throughput provided by the traditional cellular design in some of the scenarios considered.

Relay-assisted access technique has been proposed as a promising solution to improve the energy efficiency and service quality of edge users for cellular networks. In this paper, we aim to find the optimal scheduling period, optimal power allocation, and optimal user scheduling and relay selection that minimizes the total power consumption under the constraints of minimum data rate requirements for the single-cell relay-assisted cellular network. Although we assume that every user in the network is interference-free with each other due to orthogonal resource allocation, such an optimization problem is in general a mixed-integer programming, and thus the optimal solution is difficult to achieve. To make the optimization problem tractable, we decompose the problem into the power allocation optimization subproblem and the joint user scheduling and relay selection optimization subproblem. First, we obtain the optimal scheduling period approximately equal to the ratio between the number of users and the number of relays by sequentially solving these two subproblems. Furthermore, we propose a distributed joint user scheduling and relay selection algorithm based on the duality theory and auction theory. The theoretical results show that the proposed algorithm can help every user select the optimal relay and transmission time slot in polynomial time. Simulation results further show that the proposed algorithm can guarantee the minimum scheduling duration without consuming more transmit power, in comparison with other existing algorithms.

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

Visible Light Communication (VLC) combined with advanced illumination has been expected to become an integral part of next generation heterogeneous networks at the time of writing, by inspiring further research interests. From both the Cell-Centric (CC) and the User-Centric (UC) perspectives, various VLC cell formations, ranging from fixed-shape regular cells with different Frequency Reuse (FR) patterns and merged cells employing advanced transmission scheme to amorphous user-specific cells are investigated. Furthermore, different Multi-User Scheduling (MUS) algorithms achieving Proportional Fairness (PF) are implemented according to different cell formations. By analysing some critical and unique characteristics of VLC, our simulation results demonstrate that, the proposed MUS algorithms are capable of providing a high aggregate throughput and achieving modest fairness with low complexity in most of the scenarios considered.

In this paper, we study the performance of ultradensely distributed antenna system in multi-floor buildings with high user density. To reduce the pilot overhead, we consider multi-floor pilot reuse. We derive the closed-form approximations of the sum-rate for the system using linear receivers, including the linear minimum-mean-squared-error receiver and the maximal ratio combining (MRC) receiver. We demonstrate the spectral efficiency per unit volume of the system and show that the ultradensely distributed antenna system is a promising way to achieve the spectral efficiency target of 5G.

In this paper, we investigate the revenue optimization of a cellular system, which intelligently provides access services to device-to-device users (DUs) by reusing the resource-blocks (RBs) of cellular users (CUs). While charging the DUs for services, the cellular system compensates for the additional power consumption costs of the CUs to meet their required quality of service (QoS), and hence aims at achieving the best tradeoff between charging the DUs and affording the CUs' costs to maximize its own revenue. We formulate this energy-aware revenue optimization problem as a joint RB-reuse and power control problem, which we further decompose into a power control problem for each individual CU-DU pair and a DU-selection problem for selecting appropriate DUs to reuse the CUs' RBs. For each CU-DU pair, we derive the optimal power allocation in closed form and the maximum gain of the cellular system from this pair. Based on the gains of all CU-DU pairs, we then consider the DU-selection problem as maximum weighted matching on a bipartite graph and solve it by using linear relaxation. Numerical results validate our analysis regarding the optimal power allocation for each CU-DU pair and the BS's optimal selection of the DUs to reuse the CUs' RBs such that the BS's revenue is maximized.

We consider nonlinear signal processing algorithms for downlink multiple-input multiple-output (MIMO) systems with multiple-antenna users. The design goal is to improve bit error rate (BER) performance by jointly optimizing the transceiver, data streams, and user ordering, under given total transmit power. We consider a general global objective function, whose elements are Schur-convex functions of the mean square error (MSE) of each user. With nonlinear Tomlinson-Harashima precoding combined with block successive zero-forcing precoding at the transmitter, we show that the optimal nonlinear transceiver leads to favorable diagonal and parallel structures for all users with the general global performance metric. The number of data streams of each user is allowed to be an arbitrary number no more than the rank of the effective channel. We then provide closed-form expressions of the transceiver matrices and optimal power allocation of each user, and analytically characterize the optimal number of data streams of each user for the minimax and average BER metrics. The user ordering is also optimized to further improve the system performance. We also analytically investigate the impact of channel spatial correlation on the performance of our scheme and illustrate why our proposed adaptive strategy can mitigate the performance degradation caused by channel spatial correlation. The superiority of our proposed framework is demonstrated through numerical results.

This paper investigates downlink distributed power control for femtocell networks with QoS provisioning for macro-cell user equipments (MUE). Specifically, we propose two non-cooperative game formulations: the Rate Maximization Game (RMG) and the Gradient-norm Minimization Game (GMG). We treat both the macro base-station (MBS) and femto base-stations (FBS) as active players capable of adjusting their respective transmission power in response to changing environments. With the same MBS pay-off function for both games, RMG lets each femtocell maximize its rate penalized by the price paid for its transmission, whereas GMG lets each femtocell minimize a weighted norm of the “local gradient” of the Lagrangian. We propose two different categories of algorithms, the iterative-waterfilling-based algorithms and the stochastic-approximation-based algorithms, to find the corresponding Nash equilibria (NE) of both games. We also characterize sufficient conditions for the convergence of both classes of algorithms under fixed price. With proper price choice, the NEs of both RMG and GMG are related to locally optimal solutions to the system rate maximization problem with the MUE QoS constraints. To further improve system performance for FBS's, we propose two price update methods with QoS provisioning of MUEs.

Spectral efficiency (SE) and energy efficiency (EE) are both essential in future wireless communications. To improve the system performance on these two metrics, in this paper we consider the weighted sum rate maximization (WSRMax) problem subject to per-cell EE constraints and per-BS transmit power constraints in multicell multiuser downlink systems. This problem is difficult in its original form due to the introduction of new EE constraints. We first reveal that the original problem can be transformed into an equivalent parameterized polynomial form by introducing some auxiliary variables. By exploiting the concavity property with respect to each variable in the equivalent problem, an efficient block coordinate ascent algorithm is then proposed with guaranteed convergence property. Numerical results show that compared with the conventional WSRMax algorithm, in addition to fulfilling the EE requirement of each cell, our algorithm achieves a better system EE performance at the cost of a slight sum rate performance loss at a certain region and offers a new insight on the SE-EE tradeoff in wireless communication systems.

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

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

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

We consider a worst-case robust precoding design for multi-input multi-output (MIMO) communication systems with imperfect channel state information at the transmitter (CSIT). Instead of a particular choice, we consider a general imperfect CSIT model that only assumes the channel errors to be within a convex set, which includes most common imperfect CSIT models as special cases. The robust precoding design is formulated as a maximin problem, aiming at maximizing the worst-case received signal-to-noise ratio or minimizing the worst-case error probability. It is shown that the robust precoder can be easily obtained by solving a convex problem. We further provide an equivalent but more practical form of the convex problem that can be efficiently handled with common optimization methods and software packages.

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

Using imperfect channel state information (CSI) may cause severe violations of the interference restriction in cognitive radio (CR). We consider designing a robust CR system, over either SISO frequency-selective or MIMO channels, with multiple primary users (PUs) and multiple noncooperative secondary users (SUs), who form an ad-hoc network that is naturally modeled as a noncooperative game. The imperfectness of PU CSI is taken into account through the worst-case robustness philosophy. We study the existence and uniqueness properties of the Nash equilibria (NE) of the robust games, and devise distributed algorithms with their convergency properties to achieve the competitive optimality for the SU network. As special cases, our framework also provides, through convex optimization, the robust power allocation and precoding for each SU.

This paper considers robust transmit strategies, against the imperfectness of CSIT, for MIMO communication systems. Following a deterministic model that assumes the actual channel inside an ellipsoid centered at a nominal channel, we maximize the worst-case received SNR. It is shown that, for a general class of power constraints, the resulting maximin problem can be equivalently transformed into a convex problem, or even further into a semidefinite program. The most important result is that the optimal transmit directions are just the right singular vectors of the nominal channel under some mild conditions. This result reduces the complicated matrix-valued problems to scalar power allocation problems, for which the closed-form solutions are provided.