Near-field (NF) communications have attracted considerable interest, particularly with the implementation of extremely large-scale antenna arrays (ELAA). Additionally, the increase in communication frequencies and the expansion of reconfigurable intelligent surface (RIS) apertures contribute to this growing field. This paper investigates the synergy of simultaneously transmitting and reflecting (STAR)-RIS and non-orthogonal multiple access (NOMA) for secure transmission under hybrid far-field (FF) and NF scenarios. The secrecy sum rate (SSR) maximization problem is formulated by joint optimization of the power allocation, the beamforming at access point (AP), and the transmission/reflection coefficients (TRCs). Specifically, we consider the transmit power budget, unit-norm conditions, coupled phase shifts (CPS), quality of service requirements, and decoding order. To tackle this extremely challenging problem, we combine the successive convex approximation (SCA), Riemannian exact penalty method via smoothing, and penalty dual decomposition (PDD) and successfully develop an efficient iterative algorithm. Simulation results reveal that the proposed design exhibits superior effectiveness when compared to other traditional benchmarks.

To enhance the security of wireless sensing in dynamic radar environments, we propose a novel double-intelligent reflecting surfaces (IRS) assisted secure wireless sensing architecture. IRSs equipped with sensing capabilities are strategically deployed on target and clutter. A two-stage sensing strategy is adopted. In the first stage, the angles of arrival and the received signal powers at IRSs from legalized radar station (LRS)/unauthorized radar station (URS) are estimated. In the second stage, the reflection coefficients of IRSs are designed based on estimated parameters to enhance the sensing probability at LRS while simultaneously concealing the target from URS. Specifically, the reflected signals is steered towards clutter to mislead URS by generating angle-deceptive information. Optimization problems are formulated under two distinct operational modes: short-term and long-term IRS configurations. The objective is to maximize the signal power received at LRS, subject to three critical constraints, including an upper bound on the signal power received by URS from target direction, a lower bound on the signal power received by URS from clutter direction to preserve environmental masking, and the unit-modulus constraint imposed on each IRS element due to hardware limitations. To tackle the non-convex formulated problem, we first adopt semidefinite relaxation for performance benchmarking and then develop a lower-complexity iterative algorithm based on penalty dual decomposition to reduce computational burden. Simulation results demonstrate the effectiveness of the proposed secure sensing scheme in misleading URS while enhancing LRS sensing performance.

With the development of the sixth-generation (6G) communication networks, the scale of internet of things (IoT) devices is rapidly expanding. However, the limited computational and energy resources have become the major bottlenecks constraining IoT devices in processing complex tasks and providing high-quality services. In order to solve this problem, a novel unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surfaces (RIS) assisted mobile edge computing (MEC) with backscatter system is proposed. In this paper, a system energy efficient (EE) maximization problem is formulated by jointly optimizing the reflection coefficients, computational resources, time allocation, RIS phase shifts, and UAV trajectories while satisfying the task constraints, energy causality constraints, and trajectory constraints. To solve the established non-convex optimization problem, a three-stage alternating optimization algorithm is developed based on the Dinkelbach algorithm. The efficient solution of each subproblem is realized by leveraging the Lagrangian dual method, combined with the sub-gradient descent and successive convex approximation techniques. Furthermore, the closed-form expressions for the CPU computation frequency, backscatter reflection coefficient, and RIS phase-shift coefficients are derived. Simulation results show that the proposed scheme achieves superior system EE compared with the benchmark and simplified schemes.

In this paper, we investigate a millimeter-wave secure integrated sensing and communication system assisted by the sub-connected (SC) active reconfigurable intelligent surface (SC-ARIS), where the dual-function radar and communication station (DFBS) employs a hybrid precoding structure. We formulate an optimization problem to jointly design the DFBS hybrid precoding and SC-ARIS beamforming, aiming to maximize the secure energy efficiency while ensuring communication and sensing qualities. To address the above non-convex problem, we utilize alternating optimization technique to decouple it into two subproblems, where DFBS hybrid precoding and SC-ARIS beamforming are respectively optimized. For the first one, we first propose an iterative algorithm to solve the equivalent digital precoding based on constrained concave-convex procedure, Taylor expansion, semidefinite relaxation (SDR) and fractional programming techniques. Then, the hybrid precoding is obtained rely on the manifold optimization alternating minimization technique. For the later one, we propose an iterative algorithm based on the SDR. Considering a more realistic scenario, we extend to the imperfect eavesdropping channel, and propose a robust beamforming design scheme. Finally, simulation results show the effectiveness of the proposed schemes.

Intelligent reflecting surface (IRS) has been considered a promising technology to be applied to mobile edge computing (MEC) systems, especially when offloading links are blocked or weak. However, most existing works are restricted to narrow-band channel and ideal IRS reflection model, which is not practical and may lead to significant performance degradation. Thus, we consider an IRS-enhanced wideband MEC system with practical IRS reflection model. Our objective is to minimize the weighted latency of all devices by jointly optimizing the offloading data volume, edge computing resources, BS receiving vector, and IRS basic phase shift (BPS). Since the formulated problem is non-convex, we employ the block coordinate descent (BCD) technique to decouple it into two subproblems to alternatively optimize computing and communication resources. In particular, the computing resource optimization subproblem is solved based on Karush-Kuhn-Tucker (KKT) conditions and bisection search method. While the communication resource optimization subproblem is first transformed into a weighted sum-rate maximization problem based on LDR technique and KKT conditions. Then leveraging the equivalence between sum-rate maximization and MSE minimization, it is converted into a multi-variable problem that can be effectively solved using BCD technique. Simulation results show that the proposed schemes can reduce latency by 16% compared to baseline schemes when the number of IRS elements is 100, confirming the effectiveness of considering practical IRS reflection model for wideband MEC systems.

Integrated sensing and communication (ISAC) is a key technology for improving spectrum efficiency and enabling intelligent wireless networks, yet its deployment in short-packet transmission scenarios faces significant challenges such as finite block-length (FBL) effects, channel estimation uncertainty, and limited coverage. To address these issues, this paper investigates a short-packet ISAC system assisted by a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and proposes a two-stage ISAC transmission paradigm. In Stage I, the hybrid STAR-RIS performs target direction-of-arrival estimation, and a closed-form expression of Cramér–Rao Bound (CRB) is derived to establish channel state information (CSI) uncertainty model based on CRB. Meanwhile, each user performs channel estimation locally and feeds results back to DFBS. In Stage II, the estimated CSI is utilized to jointly design resource allocation, and an optimization problem is formulated to maximize target illumination power under FBL and CSI uncertainty constraints. To tackle this strongly coupled non-convex problem, we develop a hierarchical solution strategy: the sensing duration is first determined via one-dimensional search, and then, an alternating optimization framework is employed to decouple the problem into DFBS beamforming and hybrid STAR-RIS coefficient optimization, where iterative algorithms based on semi-definite relaxation, semi-definite programming, and singular value decomposition are proposed to ultimately obtain a convergent optimal solution. Simulation results validate the fast convergence and superior performance of our proposed algorithm, reveal the inherent trade-off between the two stages under constrained resources, and demonstrate the importance of joint two-stage resource design assisted by hybrid STAR-RIS in enhancing short-packet ISAC system performance.

In this paper, we design a novel two-stage transmission framework in millimeter wave-ISAC systems with multiple communication users (CUs) and multiple target scenarios. In stage I, the dual-functional base station (DFBS) performs beam scanning with pilot signals, estimating target direction of arrival angles (DoAs) through the maximum likelihood estimation and multiple signal classification techniques, while the CU estimate DoAs via minimum mean square error and MUSIC techniques. Further, we derive the closed-form Cramér-Rao Bound (CRB) expressions for estimated CU/target DoAs and establish the relationship between channel station information (CSI) error and CRB. In stage II, the DFBS transmits ISAC signals and maximizes the minimum effective signal-to-interference-plus-noise ratio (SINR) of CU by jointly optimizing two stage resources, while meeting sensing performance requirements and accounting for the impact of imperfect CSI. Since the complex interactions and strong coupling among variables, the formulated problem is non-convex and difficult to be solved directly. To address this issue, we begin by employing one-dimensional search to determine the sensing duration of Stage I. Then, based on this result, the DFBS beamforming optimization design is carried out with S-procedure method, penalty-based and successive convex approximation algorithms to convert the original problem into a tractable convex optimization problem. Finally, simulations are executed to confirm the advantages and effectiveness of our developed scheme.

ABSTRACT This paper considers a downlink multiple input single output (MISO) non‐orthogonal multiple access (NOMA) system over Nakagami‐m fading channels, where a multi‐antenna base station (BS) serves several single‐antenna users with the statistical channel state information (CSI) of each user. We propose a novel low‐complexity transmit antenna selection by head user (TAS‐head) strategy for the first time to exploit the spatial diversity of multiple antennas. Based on our proposed TAS‐head strategy, we derive a closed‐form expression of the exact outage probability (OP). We further analyse the asymptotic OP and diversity order in high signal‐to‐noise ratio (SNR) regime. Finally, we formulate a power allocation optimization problem to maximize sum throughput under outage constraints. We also design an Adam algorithm in combination with numerical differentiation method to obtain a suboptimal solution. Monte Carlo (MC) simulations verify the accuracy of our derived exact OP. Results show that our proposed TAS‐head strategy is more effective than its benchmarks (TAS‐near/far and TAS‐maj). Furthermore, we prove that PA‐TDR criterion achieves better performance than PA‐ACG in scenarios where the descending order of target data rate is the same with that of channel condition. Our designed Adam algorithm turns out to be more effective in comparison with genetic algorithm (GA) in multi‐user case. Results indicate that our proposed TAS‐head strategy is an efficient method to meet users' QoS requirements, especially in low SNR (or transmit power) regime.

Fluid antenna (FA) is capable of achieving a significant degree of spatial diversity within the limited space of a wireless device by adjusting the radiating elements to optimal positions. In this article, we explore the potential of deploying FAs on the overall performance of wireless powered communication network (WPCN). Specifically, each Internet of Things (IoT) device in WPCN is equipped with a single FA comprising multiple ports. The IoT device (ID) selects the optimal receive port for energy harvesting from the power beacon (PB), followed by choosing the optimal transmit port to send its data to the access point (AP). Our objective is to maximize the sum throughput of IDs by jointly optimizing port activation and time scheduling, subject to constraints on the received signal-to-noise ratio (SNR) of each individual ID and the total transmission time. To tackle this nonconvex problem, we first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to find the optimal solutions for time slots. Then, we introduce an efficient algorithm based on the alternating optimization (AO) method to iteratively achieve a locally optimal solution for port activation. Additionally, a low-complexity scheme is proposed to minimize computational overhead. Simulation results reveal that incorporating FAs into a WPCN markedly improves the overall system performance, and highlights the benefits of port selection for the FA in comparison to baseline methods.

In this paper, we study the robust resource optimization for the intelligent reflecting surface (IRS)-assisted full duplex (FD) integrated communication, computing and sensing (ICCS) systems. The base station (BS) serves uplink computing users, downlink communication users and sense targets simultaneously, while the imperfect channel state information (CSI) from IRS to users are considered. Next, we formulate an optimization problem of maximizing sum rate by jointly designing the BS transmit/receive beamforming, IRS reflection coefficients, computing user transmit power and local computing resources. Due to its difficulty to directly solve the formulated problem, we divide it into several subproblems and propose an alternative iterative optimization algorithm. Then, by applying the S-Procedure, singular value decomposition (SVD) and successive convex approximation techniques, each subproblem is transformed into the semidefinite programming (SDP), and the semidefinite relaxation (SDR) is applied to obtain the solution by dropping the rank-one constraint. The final solutions are obtained by alternatively solving all subproblems until convergence. Finally, the simulation results show the performance of the proposed scheme outperforms that of baseline schemes.

In this paper, we consider the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted THz communications with three-side beam split. Except for the beam split at the base station (BS), we analyze the double-side beam split at the STAR-RIS for the first time. To relieve the double-side beam split effect, we first propose a time delayer (TD)-based fully-connected structure at the STAR-RIS. As a further advance, a low-hardware complexity and low-power consumption sub-connected structure is developed, where multiple STAR-RIS elements share one TD. Meanwhile, considering the practical scenario, we investigate a multi-STAR-RIS and multi-user communication system, and sum rate maximization problem is formulated by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS as well as the double-layer phase shift coefficients, time delays and amplitude coefficients at the STAR-RISs. Based on this, we first allocate users for each STAR-RIS, and then derive the analog beamforming, time delays at the BS, and the double-layer phase shift coefficients, time delays at each STAR-RIS. Next, we develop an alternative optimization algorithm to calculate the digital beamforming at the BS and amplitude coefficients at the STAR-RISs. Finally, the numerical results verify the effectiveness of the proposed schemes.

In this paper, we investigate the near-field wideband terahertz (THz) massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems. Specifically, we propose an energy-efficient serial true-time-delay (TTD)-based reconfigurable antenna architecture for the dual-function base station (DFBS). This architecture enables DFBS to switch between modular and compact MIMO configurations by dynamically controlling the activation of antenna subarrays, corresponding to the sensing and communication stages, respectively. During the sensing stage, a modular MIMO architecture is deployed by deactivating several subarrays and all TTDs. This configuration utilizes fewer antennas to achieve a larger array aperture with enhanced energy efficiency. Moreover, we take advantage of beam squint effects at both the main and grating lobes to extend sensing range and enable rapid user sensing. During the communication stage, a compact MIMO configuration is employed by activating all antennas, and the TTD network is activated to mitigate the near-field beam squint effect. Based on the channel state information obtained during the sensing stage, we formulate a joint optimization problem of the hybrid analog/digital beamforming and TTD network time delays to maximize the system sum rate. To solve it, we first design analog beamforming and time delays through a serial-delay approach, followed by an alternating iterative optimization for digital beamforming. In addition, our proposed scheme accounts for the finite resolution and limited delay range of TTDs. Simulation results demonstrate the effectiveness of our designed antenna architecture and optimization scheme.

In this paper, the sensing beam pattern gain under simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-enabled integrated sensing and communications (ISAC) systems is investigated, in which the dual-functional base station (DFBS) provides multiple targets sensing in the presence of environment clutters and communicates with multiple users simultaneously. However, multiple targets detection introduces new challenges, since the STAR-RIS cannot directly send sensing beams and detect targets, the DFBS is required to analyze the echoes of the targets. While the echoes reflected by different targets through STAR-RIS come from the same direction for the DFBS, making it difficult to distinguish them. To circumvent this issue, we first introduce the signature sequence (SS) modulation scheme to the STAR-RIS-enabled ISAC system, thus ensuring that DFBS can detect different targets through the SS modulated sensing beams. Next, via the joint beamforming design of DFBS and STAR-RIS, we develop a max-min sensing beam pattern gain problem, and meanwhile, considering the communication quality requirements, the interference limitations of multi-targets and clutters, the passive nature constraint of STAR-RIS, and the total transmit power limitation. Then, to tackle the complex non-convex problem, we propose an alternating optimization method to divide it into two sub-problems and iteratively solve them until convergence. For the former, by relaxing the rank-one constraint, the problem is transformed into the standard convex quadratic semi-definite program and can be solved through the semi-definite relaxation and semi-definite programming algorithms. For the latter, the penalty-based algorithm is used to convert the rank-one constraints as penalty terms to the objective function, and the successive convex approximation method is leveraged to solve it. Finally, simulation results are conducted to validate the benefits and efficiency of our proposed scheme.

The communication security and covertness of legitimate vehicles in the sixth-generation (6G) mobile communication intelligent automatic transportation systems (IATS) face significant challenges, as the communication equipment and wireless signals are complex and susceptible to information leakage. To address the above issues, this paper proposes a novel IATS that integrates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA). Concurrently, an eavesdropping vehicle, capable of monitoring and eavesdropping information from legitimate vehicles, is introduced to explore the joint security and covertness performance. A roadside device is also introduced to improve security and covertness performance by transmitting artificial noise. The closed-form expressions for outage probability (OP), detection error probability (DEP), and intercept probability (IP) are derived to characterize the reliability, covertness, and security of the proposed system. The impact of various system parameters on system performance has also been extensively conducted, including transmitted signal-to-noise ratio (SNR), interference power, the power allocation coefficients, and the number of elements in STAR-RIS. The simulation results reveal several key insights: 1) The OPs and IPs of the RSMA-IATS network gradually decrease and increase with the transmitted SNR, respectively; 2) Achieving an optimal balance in power allocation between monitoring and eavesdropping proves essential for effective eavesdropper management; 3) The energy efficiency (EE) of the proposed system exhibits dual peaks at higher STAR-RIS element numbers, contrasting with a single peak at lower element counts, underscoring the strategic deployment of STAR-RIS technology in RSMA-IATS network.

In this paper, we propose a novel architecture for a multi-layer active-passive cascade transmitting reconfigurable intelligent surface (RIS)-aided receiver. We aim at enhancing the scalability of antenna dimensions and energy efficiency for user equipment (UE), as well as improving the amplitude freedom of antenna gain. This architecture integrates reflecting RIS for applications in both jamming and anti-jamming scenarios. We formulate a problem to maximize the worst-case spectral efficiency (SE) of the UE, based on imperfect channel state information (CSI) of the malicious device, thereby ensuring the SE of our UE while disrupting the signal reception of the illegal UE. To address the inherently non-convex nature of the formulated problem, we propose an alternating optimization framework, which decomposes the main problem into several subproblems. Specifically, we uniformly discretize the uncertain domains to obtain robust CSI, allowing us to determine an optimal receiver vector. To balance computational complexity and performance, we propose solutions for the subproblems of base station beamforming and coefficients with different patterns of RIS. Furthermore, to effectively disrupt malicious inter-device communication while avoiding detection and localization, we develop a collaborative interference pattern incorporating silent interference and non-interference protocols. Importantly, the pattern carefully balances performance with the overhead of CSI acquisition. Finally, simulation results validate the efficiency of the proposed algorithms, demonstrating that the proposed architecture achieves better jamming and anti-jamming performance.

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS.

The global expansion of 5G networks has led to a significant increase in network traffic. In data centers, virtual machines (VMs) must be allocated on Physical Machines (PMs) according to a specific topology. Each VM requires specific network resources to function correctly. Consolidated VM deployment can help reduce traffic consumption and prevent bandwidth-related bottlenecks, while loose deployment can minimize VM failure rates and guarantee availability during PM and switch failures. A reasonable VM deployment plan is vital to improve availability and minimize network bandwidth consumption. This paper presents four typical data center architectures, network topologies, and cost matrices extending to generality. A joint optimization model is proposed to measure Virtual Cluster (VC) risk with global availability constraints. A heuristic algorithm is then introduced to minimize the value of the constrained optimization function. The evaluation results indicate that the proposed method is effective and improves performance over the benchmarks.

With UAVs becoming increasingly prevalent across a diverse array of industries, their extensive adoption has encouraged a great increase in research attention. Combining multiple UAVs together to form UAV swarms provides increased mission performance over singular UAVs. Moreover, the use of multiple UAV’s brings the ability to distribute computation for enhanced efficiency, or widen perspectives for UAV applications with sensors. This paper proposes a novel leader-follower structured UAV swarm along with a suitable network for inter-UAV coordination and formation control. A simulation of the swarm’s kinematics will be completed using Simulink to gain insight into the accuracy of the proposed formation. Additionally a simulation of the formation control network using COOJA will be executed to investigate and analyse it’s effectiveness. Results suggest that the UAV’s have excellent tracking abilities of instructed trajectories and when incorporating the formation control network, transmission delays have a negligible effect on performance.

Transmissive reconfigurable intelligent surface (RIS) is considered as a promising technology for future wireless networks due to its low power consumption and low cost. To guarantee the user's fairness, in this paper, we propose a group-transmissive RIS scheme and formulate a max-min fairness problem via a joint optimization of the RIS transmission coefficient and power allocation. To solve it, we develop an alternating optimization algorithm to divide the original optimization problem into two subproblems. The semidefinite relaxation and successive convex approximation are used to solve each one. Then two subproblems are solved alternately until convergence and the final solutions are obtained. Finally, we present simulation results to validate the efficacy of our proposed scheme.

This article studies an intelligent reflecting surface (IRS)-empowered wireless-powered communication network (WPCN) in Internet of Things (IoT) networks. In particular, a power station (PS) with multiple antennas uses energy beamforming to enable wireless charging to multiple IoT devices, in the downlink wireless energy transfer (WET) phase; then, during the uplink wireless information transfer (WIT) phase, these IoT devices utilize the harvested energy to concurrently transmit their individual information signal to a multiantenna access point (AP), which equips with multiuser decomposition (MUD) techniques to reconstruct the IoT devices’ signal. An IRS is deployed to improve the energy collection and information transmission capabilities in the WET and WIT phases, respectively. To examine the performance of the system under study, we maximize the sum throughput with the aim of jointly designing the optimal solutions for the active PS energy beamforming, AP receive beamforming, passive IRS beamforming, and time scheduling. Due to the multiple coupled variables, the resulting formulation is nonconvex, and a two-level scheme to solve the problem is proposed. At the outer level, a 1-D search method is applied to find the optimal time scheduling, while at the inner level, an iterative block coordinate descent (BCD) algorithm is proposed to design the optimal receive beamforming, energy beamforming, and IRS phase shifts. In particular, the receive beamforming part is designed by considering the equivalence between sum rate maximization and sum mean square error (MSE) minimization, thereby deriving a closed-form solution. Furthermore, we alternately optimize the energy beamforming and IRS phase shifts using Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMMs) methods. Finally, numerical results are presented to showcase the performance of the proposed solution and highlight its advantages compared to some typical benchmark schemes.

In this paper, we investigate a millimeter wave integrated sensing and communication system with aid of the hybrid reconfigurable intelligent surface (HRIS), where the dual-function radar and communication station (DFBS) applies the hybrid precoding structure. On this basis, we consider the sensing and communication performance, respectively, and formulate two optimization problems. One is to maximize the worst-case illumination power while ensuring the communication quality, and another is to maximize the total achievable rate while satisfying the sensing performance. To solve them, we first decouple each nonconvex problem into three subproblems via the alternative optimization technique. For the former one, we transform DFBS and HRIS beamforming optimization subproblems into the convex ones by the quadratic constrained quadratic programming (QCQP) and semidefinite program relaxation (SDR) techniques, and obtain the solutions by standard convex optimization technique. For the later one, fractional programming is applied to decouple the objective function, and then we transform DFBS and HRIS beamforming design subproblems into the convex ones by QCQP and Taylor expansion techniques, and obtain the solutions by the alternating direction method of multipliers (ADMM). For the hybrid precoding design subproblems of DFBS in both problems, a manifold optimization-alternating minimization (MO-AltMin) algorithm based on minimizing the Euclidean distance is used to obtain the solutions. Simulation results show the effectiveness of the proposed schemes.

Mobile edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min–max latency optimization problem by jointly designing multiuser detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ offloading data size and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original nonconvex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation.

Wireless powered mobile edge computing (WPMEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for lowpower wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming, and uplink multiuser detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penaltybased optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

Vehicle road cooperation systems are the vital components of intelligent transportation systems, destined to play an irreplaceable role in the future smart cites. Such systems are mandated to achieve elevated data rates, ultralow latency, and enhanced reliability. To meet these requirements, we incorporate full-duplex (FD) and rate splitting multiple access (RSMA) into vehicle road cooperation systems and propose a downlink FD RSMA vehicle road cooperation system. More importantly, we introduce a crucial metric to evaluate the influence of the delay constraints on the system performance. Specifically, analytical expressions for the effective capacity of the nearby vehicle and the distant pedestrian are derived. We also provide the approximate expressions for the effective capacity at the low and high-signal-to-noise ratios (SNRs) and the upper bound on the effective capacity to gain further insights. Furthermore, we expand our evaluation to include both throughput and energy efficiency for the FD RSMA vehicle road cooperation system. Results illustrate that: the effective capacity of the nearby vehicle increases with the increasing transmitted power at low SNRs and stabilizes at a constant level at high SNRs. Conversely, the effective capacity of the distant pedestrian increases continuously with the higher transmitted power. The effective capacities of the vehicle and pedestrian are influenced by various factors, such as the transmitted power, power allocation coefficients, and Quality-of-Service exponent.

The Internet-of-Things (IoT) has greatly facilitated our daily lives. Nevertheless, how to achieve higher spectral efficiency, large-scale device access, and lower latency for the next-generation IoT is still a challenge. Inspired by this, a non-orthogonal multiple access (NOMA) assisted cognitive radio (CR) IoT network is proposed in this paper, where the communication between the indoor secondary transmitter and secondary receivers is performed in the presence of an eavesdropper and under the constraint of secondary transmit power. In particular, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the secondary network to assist the secondary transmitter to communicate with its receivers in different rooms. To characterize the reliability and security of the proposed system, we derive analytical approximate expressions for the outage probabilitys (OPs) and intercept probabilitys (IPs) by using Gaussian-Chebyshev quadrature. With the aim of providing a deeper understanding, we also explore the impacts of transmission signal-to-noise ratios (SNRs), power allocation coefficient and the number of STAR-RIS elements on the system performance. Presented numerical results show that: 1) the OPs of near and far users gradually decrease with SNRs until floors appear at high SNR, and the floors of near user is always lower than that of far user; 2) IPs increasing with SNRs and near user is always less than far user, which proves that near user has better security; 3) under appropriate parameters, the trade-off between reliability and security of the considered system can be arisen.

This paper investigates the performance of physical layer security (PLS) in wireless communication systems, where a reconfigurable intelligent surfaces (RIS) is deployed between the transmitter and legitimate receiver to enhance the communication security. The Fisher-Snedecor F distribution is adopted to model the underlying fading channels, owing to its accuracy, tractability and generality. On this basis, this paper evaluates the performance of the proposed system by deriving the average secrecy capacity (ASC) and the secrecy outage probability (SOP) under correlated Fisher-Snedecor F channel coefficients. Furthermore, the asymptotic behavior of the ASC and SOP in the high signal-to-noise ratio (SNR) regime is examined. Analyzing the correlated scenario is crucial as it provides a detailed understanding of how interdependencies among channel coefficients impact the system’s security and overall performance, offering valuable insights into real-world communication scenarios. Finally, this paper verifies the analytical results through numerical illustrations, and demonstrates the effectiveness of employing RIS.

Collaborative cloud computing (CCC) has emerged as a promising paradigm to support computation-intensive and delay-sensitive applications by leveraging MEC and MCC technologies. However, the coupling between multiple variables and subtask dependencies within an application poses significant challenges to the computation offloading mechanism. To address this, we investigate the computation offloading problem for CCC by jointly optimizing offloading decisions, resource allocation, and subtask scheduling across a multi-core edge server. First, we exploit latency to design a subtask dependency model within the application. Next, we formulate a System Energy-Time Cost ($SETC$) minimization problem that considers the trade-off between time and energy consumption while satisfying subtask dependencies. Due to the complexity of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Maximum Local Searching Offloading (MLSO) and Sequential Searching Offloading (SSO), to jointly optimize offloading decisions and resource allocation. We then model dependent subtask scheduling across the multi-core edge server as a Job-Shop Scheduling Problem (JSSP) and propose a Genetic-based Task Scheduling (GTS) algorithm to achieve optimal dependent subtask scheduling on the multi-core edge server. Finally, our simulation results demonstrate the effectiveness of the proposed MLSO, SSO, and GTS algorithms under different parameter settings.

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min security EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained concave-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves’ links, and investigate the robust security EE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the Bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP, and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.

In this paper, we investigate an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted integrated sensing and communications (ISAC) system, where the dual-functional base station (DFBS) operates in full-duplex (FD) mode to provide communication services and performs targets sensing simultaneously. Meanwhile, we consider multiple targets and multiple users scenario as well as the self-interference at the FD DFBS. Through jointly optimizing the DFBS and active STAR-RIS beamforming under different work modes, our purpose is to achieve the maximum communication sum-rate, while satisfying the minimum radar signal-to-interference-plus-noise ratio (SINR) constraint, the active STAR-RIS hardware constraints and the total power constraint of DFBS and active STAR-RIS. To tackle the complex non-convex optimization problem formulated, an efficient alternating optimization algorithm is proposed. Specifically, the fractional programming method is first leveraged to turn the original problem into a more tractable one, and subsequently the transformed problem is decomposed into several sub-problems. Next, we develop a derivation method to obtain the closed-form expression of the radar receiving beamforming, and then the DFBS transmit beamforming is optimized under the radar SINR requirement and total power constraints. After that, the active STAR-RIS reflection and transmission beamforming are optimized by majorization minimization, complex circle manifold and convex optimization techniques. Finally, the proposed schemes are conducted through numerical simulations to show their benefits and efficiency.

In this paper, we investigate the energy efficiency (EE) problem in downlink reconfigurable intelligent surface (RIS)-aided secure cell-free networks. First, we formulate a max-min secure EE (SEE) optimization problem via jointly optimizing the distributed beamforming at base stations and phase shifts at RISs under the constraint of each base station transmit power. To deal with it, we divide the original optimization problem into two sub-ones and propose an alternative scheme. Specifically, we develop an iterative optimization algorithm to solve each sub-one based on the fractional programming, constrained convex-convex procedure and semi-definite programming techniques. After that, these two sub-ones are alternatively solved until convergence, and then the final solutions are obtained. Finally, the simulation results show the effectiveness of the proposed algorithm.

Abstract Beam selection aims to select beams with large power and little inter‐user interference (IUI). In wireless communication scenarios, the levels of IUI are usually different, but, most existing beam selection methods apply the same beam selection criteria for all users, which is detrimental to maintain an attractive trade‐off between computational complexity and system performance. Considering the discrepancy of IUI comprehensively, this paper proposes a beam selection method based on interfering user grouping (IUG) for wideband millimetre‐wave massive multiple‐input multiple‐output (MIMO) systems. Firstly, a candidate beam set (CBS) is constructed in light of the sparsity and power distribution of the beamspace channel. Based on the CBS, all users are classified into non‐interfering users (NIUs), low‐interference users (LIUs) and high‐interference users (HIUs). For NIUs, the beams with large power are selected from CBS under the magnitude maximization (MM) criterion; for LIUs, a novel MM criterion with a tabu list is designed, which can effectively tackle the drawback of assigning shared beams in traditional MM; for HIUs, the incremental algorithm based on the sum‐rate maximization criterion is developed to find the optimal beams from CBS. Simulation results validate that the proposed IUG‐based method can obtain a good performance with lower computational complexity compared with the existing methods.

Nonorthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems, are combined with relay and reconfigurable intelligent surfaces (RISs). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this article, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communications have been regarded as a promising candidate for future 6G networks because of its ultra-wide bandwidth and ultra-low power consumption. However, there exists the beam split problem, especially when the base station (BS) or RIS owns the large-scale antennas, which may lead to serious array gain loss. Therefore, in this paper, we investigate the beam split and beamforming design problems in the THz RIS communications. Specifically, we first analyze the beam split effect caused by different RIS sizes, shapes and deployments. On this basis, we apply the fully connected time delayer phase shifter hybrid beamforming (FC-TD-PS-HB) architecture at the BS and deploy distributed RISs to cooperatively mitigate the beam split effect. We aim to maximize the achievable sum rate by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS and reflection coefficients at the RISs. To solve the formulated problem, we first design the analog beamforming and time delays based on different RISs’ physical directions, and then it is transformed into an optimization problem by jointly optimizing the digital beamforming and reflection coefficients. Next, we propose an alternatively iterative optimization algorithm to deal with it. Specifically, for given the reflection coefficients, we propose an iterative algorithm based on the minimum mean square error technique to obtain the digital beamforming. After, we apply Lagrangian dual reformulation (LDR) and multidimensional complex quadratic transform (MCQT) methods to transform the original problem to a quadratically constrained quadratic program, which can be solved by alternating direction method of multipliers (ADMM) technique to obtain the reflection coefficients. Finally, the digital beamforming and reflection coefficients are obtained via repeating the above processes until convergence. Simulation results verify that the proposed scheme can effectively alleviate the beam split effect and improve the system capacity.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs on the same hardware platform, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via the reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system in the mmWave band. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the radar desired waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is applied to determine the communication beamforming vector and the IRS phase shift. Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.

Mobile edge computing (MEC) offloads tasks to the MEC server located at the edge of the network, which can not only solve intensive computing but also can ensure computation with low latency. In the research of MEC, there are few research on user mobility and inter-user relevance. In this paper, we consider the task computing of relevant users in the mobile process. We combine MEC with local computing to minimize the weighted sum of user’s delay and energy consumption. First, we propose a joint optimization problem of offloading strategy and resource allocation. Then, we design an iterative algorithm based on the one-time offloading principle and delay constraints, according to the inter-user relevance and user mobility. We adopt a dichotomy to achieve resource allocation and obtain the optimal solution of the objective function. The experimental results show that the proposed iterative offloading algorithm can effectively reduce the delay and energy consumption when considering the relevance and mobility of users.

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

In this article, we propose an intelligent reflecting surface (IRS)-enabled wireless-powered caching system. In the proposed IRS model, a power station (PS) provides wireless energy to multiple Internet of Things (IoT) devices, delivering their information to an access point (AP) by utilizing the harvested power. The AP, equipped with a local cache, stores the IoT data to avoid waking up the IoT devices frequently. Meanwhile, we deploy the IRS involving in the wireless energy and information transfer process for performance enhancements. In this practical system, the PS and AP could belong to different service providers. Also, the AP requires to incentivize the PS to offer a provisional energy service. We model the interaction between the PS and AP as a Stackelberg game that jointly optimizes the transmit power of the PS, the energy price, the phase shifts of the wireless energy transfer (WET) and wireless information transfer (WIT) phases, as well as wireless caching strategies of the AP. In this way, we first derive the optimal solutions of the phase shifts and the transmit power of the PS in a closed form. We propose an alternating optimization (AO) algorithm to optimize the wireless caching strategies and the energy price iteratively. Finally, we present various numerical evaluations to validate the beneficial role of the IRS and the wireless caching strategies and the performance of the proposed scheme compared with the existing benchmark schemes.

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE.

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (BF) at the base stations and passive BF at the RISs. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CSI), we extend the proposed framework to the case that only requires part of the RIS’ CSI. This is achieved via deliberately discarding the RIS that has a small contribution to the user’s secrecy rate. Based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Meanwhile, we also study the system performance under the imperfect CSI. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones.

This paper investigates the power allocation problem for secure multiple-input single-output transmission with the injection of artificial noise (AN), in the presence of an unknown eavesdropper (Eve). Two power allocation schemes, the optimal adaptive power allocation (OAPA) and suboptimal fixed power allocation (SFPA) schemes, are proposed to enhance the physical layer security of the considered system. Since the noise power at Eve is unknown, both power allocation schemes are designed for the worst-case scenario in which the noise power at Eve is assumed to be zero, aiming to minimize the secrecy outage probability (SOP). To characterize the performance of the proposed power allocation schemes, approximate closed-form expressions for average SOP under a preset noise power level are derived by applying Gauss-Chebyshev quadrature. We also address the worst-case secrecy outage performance for the proposed OAPA and SFPA schemes. Our analytical and numerical results show that, compared with the exhaustive search method that requires Eve’s prior information, the proposed OAPA scheme exhibits comparable secrecy outage performance without Eve’s prior information. Additionally, the SFPA scheme, also without Eve’s prior information, is capable of achieving almost the same worst-case SOP as the OAPA scheme, with a much lower implementation complexity.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future Internet-of-Things (IoT) users due to its ultrawide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input-multiple-output (MIMO) represent two effective solutions for compensating the large path loss in THz systems. In this article, we consider an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station (BS) and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation (SDR) technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated nonconvex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.

This paper investigates the impact of intelligent reflecting surface (IRS) enabled wireless secure transmission. Specifically, an IRS is deployed to assist multiple-input multiple-output (MIMO) secure system to enhance the secrecy performance, and artificial noise (AN) is employed to introduce interference to degrade the reception of the eavesdropper. To improve the secrecy performance, we aim to maximize the achievable secrecy rate, subject to the transmit power constraint, by jointly designing the precoding of the secure transmission, the AN jamming, and the reflecting phase shift of the IRS. We first propose an alternative optimization algorithm (i.e., block coordinate descent (BCD) algorithm) to tackle the non-convexity of the formulated problem. This is made by deriving the transmit precoding and AN matrices via the Lagrange dual method and the phase shifts by the Majorization-Minimization (MM) algorithm. Our analysis reveals that the proposed BCD algorithm converges in a monotonically non-decreasing manner which leads to guaranteed optimal solution. Finally, we provide numerical results to validate the secrecy performance enhancement of the proposed scheme in comparison to the benchmark schemes.

With the increasing development of cloud computing and wireless technology, mobile cloud computing has been developed to alleviate the limitation of battery capacity and computing capability of the mobile device by offloading some computation‐intensive tasks onto the cloud. However, the extra consumption for transmission from the mobile device to the remote cloud may lead to degradation of performance. To this end, the authors develop a Markov decision process‐based computation offloading (MDPCO) algorithm to minimise the energy efficiency cost (EEC) from a global perspective by jointly optimising the resource allocation and offloading decisions. Firstly, they formulate an EEC minimisation problem for a single‐chain application with M tasks. Due to the difficulty to directly solve the formulated problem, they decompose it into multiple subproblems and preferentially optimise the local computing frequency and transmission power by distributed algorithm under hard time constraints. Based on this, they proposed the Markov decision process‐based offloading algorithm to preschedule the computing side for each task from a global perspective to minimise the EEC further. The simulation results show that the performance of the MDPCO algorithm is significantly superior to that of the other algorithms under different parameters.

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three‐layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed‐form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

Nowadays, advanced smart mobile devices equipped with multi‐core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single‐thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi‐core resources, this study proposes a fine‐grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi‐core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine‐grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing.

This article exploits a generic downlink symbiotic radio (SR) system, where a base station (BS) establishes a direct (primary) link with a receiver having an integrated backscatter device (BD). In order to accurately measure the backscatter link, the backscattered signal packets are designed to have finite block length. As such, the backscatter link in this SR system employs the finite blocklength channel codes. According to different types of the backscatter symbol period and transmission rate, we investigate the noncooperative and cooperative SR systems, and derive their average achievable rate of the direct and backscatter links, respectively. We formulate two optimization problems, i.e., transmit power minimization and energy-efficiency maximization. Due to the nonconvex property of these formulated optimization problems, the semidefinite programming (SDP) relaxation and the successive convex approximation (SCA) are considered to design the transmit beamforming vector. Moreover, a low-complexity transmit beamforming structure is constructed to reduce the computational complexity of the SDP relaxed solution. Finally, the simulation results are demonstrated to validate the proposed schemes.

In this article, we consider an edge cache-assisted millimeter-wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can prefetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their required files either from the local caches or from the central processor via multicast fronthaul links. For such a scenario, we formulate a beamforming design problem to minimize the secure transmission delay under transmit power constraint of each RRH. Due to the difficulty of directly solving the formulated problem, we divide it into two independent ones: 1) minimizing the fronthaul transmission delay by jointly optimizing the transmit and receive beamforming and 2) minimizing the maximum access transmission delay by jointly designing cooperative beamforming among RRHs. An alternatively iterative algorithm is proposed to solve the first optimization problem. For the latter, we first design the analog beamforming based on the channel state information of the actuators. Then, with the aid of successive convex approximation and $S$ -procedure techniques, a semidefinite program (SDP) is formulated, and an iterative algorithm is proposed through SDP relaxation. Finally, the simulation results are provided to verify the performance of the proposed schemes.

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

In this paper, we investigate spectrum sharing ultra- reliable and low- latency communications (URRLC) in an un- manned aerial vehicle (UAV)-aided cognitive radio (CR) internet of thing (IoT) network. Particularly, the secondary IoT devices opportunistically accesses the radio resource provided by a primary network and directly transmits short packets to the mobile UAV. A novel performance metric is proposed with finite block-length codes is adopted in the secondary UAV-aided IoT network. We aim to maximize the minimum average finite block-length rate for the secondary UAV-aided IoT network, subject to a probabilistic interference power constraint to the primary network based on imperfect channel state information (CSI). This formulated problem is non-convex due to the binary time scheduling, the power allocation, and the UAV altitude. In order to circumvent this issue, we develop an alternating method to solve this problem. Specifically, we first exploit the time scheduling optimization of the IoT devices for given power allocation and UAV altitude. Next, the monotonicity of the average finite block-length rate is analyzed to gain more insights for given time scheduling and UAV altitude. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the achievable average rate in the finite block- length regime. The optimal altitude of the UAV can be obtained by one-dimensional line search. Numerical results validate the effectiveness and accuracy of the derived theoretical results.

Low latency and energy efficiency are two important performance requirements in various fifth-generation (5G) wireless networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we investigate the hybrid precoding design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS). Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.

Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Different from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is difficult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.

In this paper, Generalized Space-Time Block Coded Spatial Modulation (GSTBC-SM) is proposed for Multiple-Input and Multiple-Output (MIMO) system, which can be extended into an arbitrary even number of Transmit Antennas (TAs). The proposed GSTBC-SM scheme employs the hybrid concepts of Generalized Space-Time Block Coding (GSTBC) and Spatial Modulation (SM) to further exploit the diversity benefits of GSTBC using sparse Radio Frequency (RF) chains. To be more specific, the information bits are divided into Nugroups and each group is modulated by SM scheme. Finally, the Nusymbols are invoked for GSTBC structure. In order to demonstrated the advantages of our proposed GSTBC-SM schemes, the theoretical Average Bit Error Probability (ABEP) of our proposed GSTBC-SM is derived. Both our analytical and simulation results demonstrated that the proposed GSTBC-SM scheme is capable of providing considerable performance gains over the corresponding GSTBC schemes at the same transmit rate associated with the same number of RF chains.

The number of wireless devices is growing rapidly on a daily basis echoing the increasing number of applications of the Internet of Thing. Facing massive connections and unavoidable interference, how to provide a green communication is a concerning matter. In this regard, nonorthogonal multiple-access (NOMA) is a natural communications technology that can scale with the massive number of simultaneous connections for a limited bandwidth. In this paper, we aim to maximize the energy efficiency (EE) for an NOMA-based cloud radio access network, where sub-6 GHz and millimeter wave bands are used in fronthaul and access links, respectively. In particular, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form. A two-layer algorithm is then proposed. In the outer loop, the ℓ1-norm technique is adopted to transform the nonconvex fronthaul capacity constraint into a convex one, whereas in the inner loop, the weighted minimum mean square error approach is applied. Simulation results indicate that the proposed NOMA scheme can obtain higher EE as well as throughput when compared with orthogonal multiple-access methods.

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

This paper investigates a two-tier heterogeneous networks (HetNets), where millimeter wave (mmWave) frequency is employed at the macro base station (MBS), and the small cell BSs (SBSs) consider orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is studied to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users' quality of service (QoS) constraints. Due to nonconvexity of the mixed integer nonlinear fraction programming (MINLFP), the formulated problem cannot be solved directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we reformulate the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is adopted to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal power allocation. Furthermore, the convergence of the proposed iterative algorithm is analyzed. Numerical results are presented to demonstrate our proposed algorithms.

In this paper, we investigate pilot allocation problem in two-tier time division duplex (TDD) heterogeneous network (HetNet) with mMIMO. First, we propose a new pilot allocation scheme to maximize ergodic downlink sum rate of macro users (MUs) and small cell users (SUs), where the uplink pilot overhead and cross-tier interference are jointly considered. Then, we theoretically analyze the formulated problem and propose a low complexity one-dimensional search algorithm to obtain the optimum pilot allocation. In addition, we propose two suboptimal pilot allocation algorithms to simplify the computational process and improve SUs' fairness, respectively. Finally, simulation results show that the performance of the proposed scheme outperforms that of the traditional schemes.

In this paper, we investigate the dynamic small cell (SC) clustering strategy and their precoding design problem for interference coordination in two-tier heterogeneous networks (HetNets) with massive MIMO (mMIMO). To reduce interference among different SCs, an interference graph-based dynamic SC clustering scheme is proposed. Based on this, we formulate an optimization problem as design precoding weights at macro base station (MBS) and clustered SCs for maximizing the downlink sum rate of SC users (SUs) subject to the power constraint of each SC BS (SBS), while mitigating inter-cluster, eliminating inter-tier, intra-cluster and multi-macro user (MU) interference. To eliminate the inter-tier and multi-MU interference simultaneously, we propose a clustered SC block diagonalization precoding scheme for the MBS. Next, each SU's precoding vector at clustered SCs is designed as the product of the following two parts. The first part is designed with singular value decomposition to remove the intra-cluster interference. The second part is designed to coordinate the inter-cluster interference for maximizing the downlink sum rate of SUs, which is a non-convex optimization problem and difficult to solve directly. A non-cooperative game-based distributed algorithm is proposed to obtain a suboptimal solution. Meanwhile, we prove the existence and uniqueness of Nash equilibrium for the formed game. Finally, simulation results verify the effectiveness of our proposed schemes.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

Pilot contamination due to the pilot reuse in adjacent cells is a serious problem in time-division duplex (TDD) massive multi-input multiple-output (MIMO) system. Therefore, the pilot allocation is significant for improving the performance of the system. In this paper, we formulate the pilot allocation optimization problem for maximizing uplink sum rate of the system. To reduce the required complexity for finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while fixing the pilot allocation in other cells. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate a fairness aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms obtain good performance comparable to the exhaustive search algorithm, meanwhile the users' fairness is improved.

Multicast technology will play a very important role in the future multimedia communication application. In this paper, a new transmission mechanism called multi-group multicast (MGMC) based on inter-group and inner-group cooperation is proposed for cognitive networks. The new transmission mechanism sets multiply multicast groups as a pair, and then these multicast groups transmit information in a cooperative way by using the same frequency resource. We formulate a weighted overall rate optimization problem with interference constraints to the primary user (PU) and peak power constraint at each user and obtain the optimal solution by theoretical analysis in this new transmission model. Numerical results show the impact of user number on rate for every multicast group.

This study investigates sensing‐based spectrum sharing access (SSSA) and sensing‐based spectrum opportunistic access (SSOA) schemes in cooperative orthogonal frequency division multiplexing (OFDM)‐based cognitive radio networks with imperfect spectrum sensing. The optimal resource allocation strategy, including sensing time and transmit power, is designed to maximise the ergodic throughput of the secondary system. For a two‐hop cooperative communication in the secondary system, the authors adopt the amplify‐and‐forward relay protocol and enable the source and relay to sense the state of the primary user jointly. To protect the PU effectively from harmful interference, they consider the average interference power constraint in each hop. The total average transmit power constraint of the source and relay is considered. Two simplified versions for the SSSA and SSOA schemes are employed because of the complexity of the problem. They then propose two algorithms that acquire the optimal sensing time and power allocation for both schemes. Finally, simulation results are presented to compare the performance of the two schemes.