To solve the problems of low system security and poor transmission quality in integrated satellite-terrestrial communication systems due to eavesdroppers and channel uncertainties, a robust resource allocation (RA) problem is studied. First, considering the constraints of users’ quality of service, the maximum transmit power of the satellite and the base station (BS), and the bounded channel uncertainties, an RA optimization problem is established by jointly optimizing the beamforming vectors, artificial noise (AN) vectors, and power allocation factors. Then, S-Procedure, successive convex approximation (SCA), and alternating optimization are adopted to convert the nonconvex problem with parameter perturbation into a convex one that can be solved efficiently. Finally, a robust RA algorithm based on the alternating approach is proposed to obtain the solutions. Simulation results show that the proposed algorithm has good security and robustness, and the outage probability is reduced by 9.12% compared to the traditional nonrobust algorithms without AN.

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.

Movable antennas have recently emerged as a promising paradigm to overcome the inherent inflexibility of conventional fixed antenna arrays. By enabling the physical movement of antenna elements, movable antennas introduce additional spatial degrees of freedom to wireless systems. Although the importance of the movement delay has been recognized, a critical yet unexplored problem is that the movement schemes used to transition from the initial to the target positions are overlooked. This paper presents a systematic investigation of two fundamental movement schemes: concurrent movement and cumulative movement, and addresses a key design question: Should we prioritize minimizing the total configuration time or maximizing the communication performance under a limited movement budget? Specifically, two different optimization problems are formulated to maximize the sum rate under different movement constraints, thereby introducing tighter coupling between antenna positions and beamforming design, increasing computational complexity in joint optimization, and necessitating efficient allocation of delay budgets across multiple antennas. To this end, we develop an alternating-optimization-based algorithm to obtain the corresponding suboptimal solutions. A theoretical degeneration analysis is further conducted to provide fundamental insights. The optimal strategy for a single antenna can surprisingly be to not move. While in multi-antenna systems, the performance gap scales with antenna displacement, movement budgets, and transmit power. Simulation results show that movable antennas substantially improve achievable rates over fixed antennas, with concurrent movement benefiting low-latency scenarios, while cumulative movement favoring high-rate or delay-tolerant scenarios.

In millimeter wave (mmWave) communication systems, beam alignment is doomed to play a vital role in ensuring directional link performance. In this paper, we propose a novel transformer-based angle prediction scheme to achieve fast and effective beam alignment. Transformer is one of the hottest seq2seq models in recent times, which is utilized to build the mapping relationship between the geographic position and beam alignment angles of users in this paper. Simulation results demonstrate the performance of the proposed scheme in terms of prediction accuracy and achievable sum rate.

Resource allocation (RA) has been considered as a key technique to achieve the optimal system performance in symbiotic radio (SR) systems by optimizing system parameters. However, most of the existing works only consider the ideal hardware conditions or perfect channel information, where the system performance of the above algorithms may be degraded under imperfect channel state information (e.g., channel estimation errors) and unideal hardware conditions (e.g., distortion noises). In order to improve transmission robustness and information security, in this paper, we study the robust secure RA problem for a multiple-input single-output SR system under channel uncertainties and hardware impairments (HWIs) with an eavesdropper. The robust RA problem with bounded channel uncertainties is formulated to maximize the total energy efficiency (EE) of the system under the minimum energy-harvesting constraint of each backscatter device (BD), the maximum transmit power constraint of the primary base station, the minimum secrecy rate of each BD, the decoding constraint, as well as the reflection coefficient constraint. To address the non-convex optimization problem, the original robust RA problem with the infinite constraints is converted into a deterministic one via a worst-case approach. Then, the objective function is transformed into a non-fractional form by using the Dinkelbach’s method. After that, the above problem is converted into a convex problem based on S-Procedure and the eigenvalue decomposition approach, and an iterative-based robust secure RA algorithm is proposed via an alternating optimization principle. Simulation results demonstrate that the proposed algorithm has lower outage probabilities and higher EE compared to the non-robust algorithm and the RA algorithm without HWIs.

In this paper, we consider a generalized space-air-ground integrated Internet-of-remote-things system with multiple unmanned aerial vehicles (UAVs) and low earth orbit satellites. To explore the diverse channel propagation conditions and adapt to the practical transmission environment, we investigate the three-dimensional node association among sensors, UAVs, and satellites, the spectrum partition between two-hop data collection links, and the multi-UAV deployment under the probabilistic ground-to-air channel model. Unlike existing works, we address the issue of user fairness by maximizing the minimum amount of collected data among all sensors. To cope with the formulated mixed-integer non-convex problem, we decompose it into two subproblems: a node association and spectrum partition subproblem, and a UAV deployment subproblem. To enhance optimized performance, the above two subproblems are solved alternately using the Lagrange dual decomposition and sequential quadratic programming. Simulations show that the proposed strategy converges within 15 iterations and yields an efficient solution, incurring an average loss of approximately 0.2 percent compared to the result of a brute-force search-based algorithm. Additionally, it outperforms benchmarks based on variable relaxation, successive convex approximation, and deep reinforcement learning under various parameter settings.

To overcome the impact of information leakage, obstacle blocking, channel uncertainties, and hardware impairments (HWIs) in wireless communication systems, we design a robust secure transmission strategy for a multi-reconfigurable intelligent surface (RIS)-aided communication system with HWIs and channel uncertainties, where a multi-antenna base station (BS) serves multiple wireless users aided by multiple RISs and overcomes information leakage caused by multiple eavesdroppers. Based on bounded channel uncertainties, a total transmit power minimization problem is investigated subject to the secrecy rates of users, the maximum transmit power of the BS, and the phase shifts of RISs. To deal with the formulated non-convex problem with parameter perturbations, it is transformed into a deterministic problem by using the worst-case approach, S-procedure, and successive convex approximation. Then, the problem is decomposed into an active beamforming and artificial noise subproblem and a passive beamforming subproblem. The subproblems are converted into convex ones via the semi-definite relaxation method, singular value decomposition, penalty function, and eigenvalue decomposition approaches. Finally, an iteration-based robust resource allocation algorithm is proposed. Simulation results verify that by deploying more RISs or increasing the number of reflection elements, the impacts of eavesdroppers and HWIs can be effectively decreased even with channel estimation errors.

In order to reduce the impact of obstacles and improve system performance for traditional backscatter communication (BackCom) networks, we propose a reconfigurable intelligent surface (RIS)-assisted heterogeneous BackCom network framework, where multiple backscatter clusters share the spectrum resource with macrocell users in an underlay spectrum sharing mode and achieve self-sufficient energy of each low-power-consumption backscatter device (BD) via a radio-frequency energy-harvesting way. Then, a robust resource allocation problem with imperfect channel station information is studied under the constraints of the minimum rate requirement of each BD, the minimum energy requirement of each BD, the maximum interference power of each macrocell user, the reflection coefficient of each BD, and the phase shifts of each RIS. Moreover, based on the bounded channel uncertainty model, a max-min throughput resource allocation problem of multiple backscatter clusters is formulated by jointly optimizing the time allocation factors, the reflection coefficient of each BD, and the phase shifts of each RIS. To deal with the non-convex optimization problem caused by the uncertain constraints and non-convex constraints, the worst-case approach, successive convex approximation as well as semi-definite relaxation are applied. Finally, an iteration-based robust resource allocation algorithm is proposed accordingly. Simulation results demonstrate that the proposed algorithm has good fairness and stronger robustness.

Mobile edge computing (MEC)-assisted vehicle-to-everything (V2X) communication has been proposed as it can reduce the computation overhead of vehicles by offloading partial tasks. However, the performance improvement of such systems is still challenging due to the limited spectrum resources and computation capabilities. To this end, we study a mode selection and resource allocation (RA) problem in MEC-assisted V2X networks with limited energy and bandwidth constraints. Our goal is to minimize the delay of vehicle-to-infrastructure (V2I) links under the constraints of the maximum transmission bandwidth, the minimum data rate, the maximum transmit power, and the mode selection factors. To solve the mixed-integer nonlinear programming problem, an alternative optimization method is employed to decompose it into two subproblems: a radio RA subproblem and a task offloading subproblem. Then, the former subproblem is converted into a convex problem via the variable substitution approach, while the latter one is converted into a convex problem via variable relaxation and successive convex approximation. Finally, an iteration-based RA algorithm is proposed. Simulation results show that the proposed algorithm reduces latency by 77.9% compared to the RA algorithm without MEC and by 68.9% compared to the RA algorithm without mode selection.

To utilize the limited acoustic spectrum while combating the harsh underwater propagation, we incorporate partial spectrum sharing into an underwater acoustic sensor network and aim to maximize the minimum data collection rate among all underwater sensor nodes through joint power allocation and spectrum assignment. To cope with the non-convex optimization problem, we propose a Hybrid Model-based and Data-based Resource Allocation (HMDRA) scheme: 1) Under any given spectrum assignment strategy, we analyze the impact of the partial spectrum sharing and imperfect successive interference cancellation on baseband signal processing, and formulate a power allocation problem that is solved by the bisection method and Lagrange dual theory. 2) Based on the optimal power allocation strategy, the gradient-free genetic algorithm (GA) is first adopted to approach the optimal solution of the model-less spectrum assignment problem by nearly enumerating the solution space. To reduce complexity, we further propose a deep reinforcement learning (DRL)-based algorithm and obtain an efficient solution by traversing a deep neural network-based policy learned from the training stage. Simulation results show that compared with the GA-based algorithm, the average execution time of the DRL-based algorithm is substantially reduced by 5 orders of magnitude to 0.7076 seconds at the cost of approximately 6 percent performance loss.

Abstract Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies.

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.

Due to its almost zero power consumption and low-hardware costs, backscatter technology has recently gained considerable attention. However, traditional backscatter systems employ basic binary modulations to transmit data at a relatively low rate. In this article, we introduce VITAS, a new backscatter system that enables the tag to communicate with the transceiver using versatile modulations at a megabit rate. First, we present the design of the tag, which supports both binary and higher order modulations. We also develop a series of assembly instructions for the tag controller to generate modulated symbols with minimal clock cycles, thereby enhancing the data rate for a given clock frequency. Then, we design the transceiver to coordinate the backscatter communications with the tag. The transceiver incorporates a specific symbol synchronization algorithm to address the accumulated timing errors resulting from the tag unstable clock during high-rate transmission. Furthermore, we fabricate the tag on a four-layer acrlong PCB and implement the transceiver on a acrlong USRP X310. Finally, we provide experimental results to show that the VITAS system is capable of transmitting acrshort QPSK symbols at a maximum rate of 3 Mbit/s while consuming only$9.63~\mu \text{W}$(3.21 pJ/bit).

Cognitive backscatter communication (BackCom) is a promising technology for improving the spectrum- and energy-efficiency of Internet of Things by enabling spectrum sharing and energy saving. However, the performance of cognitive BackCom networks is adversely affected by the mutual interference between the primary and secondary systems and the blocked links caused by obstacles. Additionally, assuming perfect channel state information (CSI) is unrealistic in practical cognitive BackCom networks due to the limited signal processing capabilities of cognitive backscatter nodes (CBNs) and channel delays. To address these challenges, we investigate a robust radio resource allocation and passive beamforming problem for a downlink reconfigurable intelligent surface (RIS)-enhanced cognitive BackCom network under the nonlinear energy-harvesting (EH) model and imperfect CSI. In particular, a primary base station serves multiple primary users (PUs), while multiple pairs of CBNs share the spectrum of PUs to communicate with each other in a harvest-then-transmit way. Our goal is to maximize the total energy efficiency (EE) of CBNs subject to the constraints of maximum interference power, minimum EH, time allocation, and the phase shift of the RIS. To solve the nonconvex optimization problem, we propose an iteration-based EE optimization algorithm that leverages methods of quadratic transform, variable substitution, and semidefinite relaxation. Simulation results verify that the proposed algorithm has improved its EE by 11.39% and reduced outage probabilities by 15% compared to the existing algorithms.

To alleviate the blockage effect and explore the potential of reconfigurable intelligent surface (RIS) assisted communication, we investigate the cooperative double-RIS assisted multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. To improve system performance, we jointly optimize the transmit beamforming matrix at the base station and the phase shift matrices at RISs to maximize the system sum rate, which is an intractable non-convex problem. To solve the problem, we propose an efficient alternating optimization algorithm based on the techniques of weighted minimum mean square error (WMMSE), Lagrange multiplier and majorization-minimization (MM). Simulation results validate the effectiveness of the proposed algorithm, as well as the superiority of double-RIS in improving system performance.

Signals over millimeter wave (mmWave) bands suffer from severe path loss and are easily blocked by obstacles, which greatly degrades the link quality and reliability of mmWave communications. One of the promising ways to overcome this challenge is multi-connectivity, which enables a user to associate with multiple small cells simultaneously. In this paper, we investigate the association problem of a given user to multiple mmWave base stations (mBSs), which we termed user-centric association. In particular, we consider an intelligent reflecting surface (IRS)-aided ultra-dense mmWave communication system, in which multiple distributed IRSs are deployed to expand the coverage of mmWave signals to blind spots. The user-centric association problem is formulated to maximize the user achievable sum-rate with respect to the mBS-user association, auxiliary IRS selection, and power allocation in a combinatorial manner. The original optimization problem is a mixed-integer nonlinear programming problem, which is NP-hard. To solve it, we first relax the formulated problem into a continuous one, then decouple it into three subproblems by utilizing decomposition technique, and finally propose an alternating iteration based algorithm to obtain the optimal solution. Numerical simulations show that the user sum-rate can be greatly improved by the joint optimization scheme.

How to achieve reliable communication and safety monitoring is very important in coal mines. However, most of the existing transmission strategies and sensing-based monitoring approaches assume a single objective and neglect non-line-of-sight (NLOS) problems brought by winding tunnels or mine collapses. To this end, we first propose a multihop reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) approach to maximize the energy efficiency of the JCAS access point and the sum sensing rates in order to improve the sensing accuracy. Specifically, we formulate an energy-efficient optimization problem by jointly designing both the phase-shift matrix and the switches status of the RISs as well as the transmit power of the access point. The problem is solved by adopting the successive convex approximation-based alternating optimization algorithm, the second-order optimization method, the Lambert-$w$function, and Newton’s method. Moreover, a sensing-based rate optimization problem is also solved via the Lagrange relaxation method and the Gradient descent method. Simulation results demonstrate that the proposed algorithm has better robustness and higher energy efficiency.

In this article, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of Things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching- and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are, respectively, formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we, respectively, develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding suboptimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.

Cognitive radio and backscatter communication (BackCom) have been viewed as two promising technologies for the future green Internet of Things (IoT). The combination of these two technologies can not only enhance spectrum efficiency but also improve energy efficiency. However, most of the existing resource allocation (RA) algorithms in cognitive BackCom networks consider ideal channel state information and a time division multiple access protocol, which is unrealistic in practical systems and can not support the massive number of accessing users. To this end, in this article, we study a robust chance-constrained RA problem for nonorthogonal multiple access (NOMA)-based cognitive BackCom networks to overcome the influence of channel estimation errors and support for large-scale IoT nodes. Specifically, cognitive backscatter users (CBUs) can not only share the spectrum resource owned by primary users but also harvest surrounding radio frequency and transmit their own information over the primary signals. Moreover, CBUs can use the harvested energy to actively transmit information via an NOMA protocol. The robust RA problem with outage-probability constraints is formulated to maximize the total throughput of CBUs by jointly optimizing the transmission time, transmit power, and the reflection coefficients of CBUs. To tackle the nonconvex problem, the original problem is converted into an equivalent form by applying the linear objective function, an inequality transformation approach, and an auxiliary variable method. Then, an iteration-based RA algorithm is proposed to solve it. Simulation results demonstrate the effectiveness of the proposed algorithm by comparing it with the benchmark algorithms.

Backscatter communication (BackCom), one of the core technologies to realize zero-power communication, is expected to be a pivotal paradigm for the next generation of the Internet of Things (IoT). However, the “strong” direct link (DL) interference (DLI) is traditionally assumed to be harmful, and generally drowns out the “weak” backscattered signals accordingly, thus deteriorating the performance of BackCom. In contrast to the previous efforts to eliminate the DLI, in this paper, we exploit the constructive interference (CI), in which the DLI contributes to the backscattered signal. To be specific, our objective is to maximize the received signal-to-noise ratio (SNR) by jointly optimizing the receive beamforming vectors and tag selection factors under different detection error probability (DEP) requirements, which leads to two different optimization problems. However, the resulting problems are non-convex and unanalyzable due to constraints on the DEP. To solve these problems, the Kullback-Leibler divergence is first applied to transform the DEP into a tractable form. Then, inspired by the alternating optimization, we respectively propose two successive convex approximation (SCA)-based algorithms to solve the corresponding sub-problems with beamforming design, and a greedy algorithm to solve the sub-problem with tag selection. In order to gain insight into the CI, we consider a special case with the single-antenna reader to reveal the channel angle between the backscattering link (BL) and the DL, in which the DLI will become constructive. Simulation results show that significant performance gain can always be achieved with the proposed algorithms compared to the traditional algorithms without the CI in terms of the received SNR. The derived constructive channel angle for the BackCom system with a single-antenna reader is also confirmed by simulation results.

Resource allocation is very important for achieving interference suppression and protecting the quality of service of users in heterogeneous networks (HetNets). However, the existing works with perfect channel state information (CSI) and ideal hardware ignored the impact of channel uncertainties and hardware impairments on system performance. In this paper, we design a robust secure resource allocation algorithm with imperfect CSI to achieve the energy efficiency (EE) maximization of femtocell users for a two-tier downlink HetNet with multiple passive eavesdroppers, where the residual hardware impairments are considered at the transceivers. The formulated EE problem is non-convex with the consideration of the maximum transmit power constraint of each base station, the cross-tier interference power constraint, as well as the secure rate constraint. By using the worst-case approach and successive convex approximation, the resource allocation problem with the infinite-dimensional constraints is converted into a convex one which is efficiently solved by using convex optimization theory. Simulation results verify that the proposed algorithm has a higher EE and causes less interference power to macrocell users by comparing it with the baseline algorithms.

The transmission performance of an reconfigurable intelligent surface (RIS)-aided device-to-device (D2D) communication network is fundamentally limited by the devices' energy. To address this challenge, in this paper, the joint radio resource allocation of D2D users (DUs) with piece-wise linear energy harvesting (EH) models and passive beamforming of the RIS is investigated to maximize the sum rate of DUs for an RIS-aided wireless-powered D2D communication underlaying a cellular network. Specifically, multiple wireless-powered DUs harvest radio-frequency energy from a hybrid access point (HAP) with the help of an RIS during the EH phase and achieve data transmission by using the harvested energy during information transmission phase. The optimization problem is formulated by jointly optimizing the transmit power of DUs, transmission time, the active beamforming vector of the HAP, and the passive beamforming matrix of the RIS. An alternating optimization-based algorithm is designed to solve the non-convex problem by using the variable substitution approach and the Lagrangian dual method. Simulation results have shown that our proposed algorithm provides a significant improvement in data rates over the existing algorithm without the RIS.

Robustness and energy efficiency (EE) are of crucial importance in reconfigurable intelligent surface (RIS)-assisted wireless communication networks. However, a large portion of the current works assume that perfect channel state information (CSI) can be obtained, which is impractical because of the passive features of the RIS and the lack of radio frequency chains at the RIS. To handle this issue, we investigate an alternating optimization (AO) algorithm in an RIS-assisted non-orthogonal multiple-access (NOMA) network with imperfect CSI. The EE-based maximization resource allocation problem is formed with the maximum transmit power constraint at the base station, the continuous phase shifts constraint of the RIS, and the outage probability constraint of the signal-to-interference-noise ratio. To solve the tricky non-convex fractional problem, Dinkelbach’s method is used to convert the fractional objective function into parameter subtraction form, and the S-procedure is utilized to deal with the non-convex outage probability constraint with channel uncertainties. Moreover, by applying the AO algorithm the original optimization problem is converted into several semi-definite programming (SDP) subproblems. The overall simulation results illustrate that the proposed algorithm has good robustness and EE.

The energy efficiency (EE) of femtocells is always limited by the surrounding radio environments in heterogeneous networks (HetNets), such as walls and obstacles. In this paper, we propose to deploy reconfigurable intelligent surfaces (RISs) to improve the EE of femtocells. However, perfect channel state information is more difficult to obtain due to the passive characteristics of RISs and non-cooperative relationship between different tiers. Besides, the low-cost transceivers and reflecting units suffer nontrivial hardware impairments (HWIs) due to the hardware limitations of practical systems. To this end, we investigate a realistic robust beamforming design based on max-min fairness for an RIS-aided HetNet under channel uncertainties and residual HWIs. The joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase-shift matrices of RISs is formulated as a non-convex problem to maximize the minimum EE of the femtocell subject to the constraints of the maximum transmit power of FBSs, the quality of service of users, and unit modulus phase-shift constraints of RISs. We develop an iterative block coordinate descent-based algorithm which exploits the semi-definite relaxation, the S-procedure, and the singular value decomposition method. Simulation results reveal that the proposed algorithm outperforms existing algorithms in terms of fairness, EE, and outage probability.

The industrial Internet of Things (IIoT) is one of the key applications in 5G heterogeneous networks. To support high energy efficiency (EE) and reliability of IIoT equipment, it is important to design an efficient resource allocation algorithm in dynamic and complex environments. However, most of the studies on 5G heterogeneous IIoT networks did not address the transceiver hardware impairment (HWI) issues (e.g., phase noises, amplifier nonlinearities, and quantization errors) and the corresponding algorithms may not be applicable in practice. To this end, in this article, we investigate a realistic beamforming algorithm in a multicell downlink multiple-input single-output heterogeneous IIoT network by incorporating HWIs in our design. In particular, a beamforming design problem is formulated as a nonconvex optimization problem for maximizing the total EE of all equipment subject to the quality of service constraints of the IIoT equipment in both the macrocell and femtocells and the maximum transmit power constraints of base stations. In light of the intractability of the considered problem, we develop an EE-based iterative beamforming algorithm to tackle the formulated problem by employing the semidefinite relaxation method, Dinkelbach’s method, and the successive convex approximation method. Simulation results show that the proposed algorithm can achieve higher EE and bring less interference power to the macrocell IIoT equipment by comparing it with baseline algorithms.

Beamforming design is crucial to reconfigurable intelligent surface (RIS)-aided communication networks. However, most of the existing works assume ideal hardware and perfect channel state information (CSI), which are unrealistic assumptions in practical systems. In order to improve system robustness and user fairness, in this paper, we firstly study the max-min energy efficiency problem for RIS-aided heterogeneous networks under non-ideal hardware and imperfect CSI. Specifically, the joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase shift matrices of RISs is formulated as a nonconvex problem to maximize the minimum energy efficiency of femtocells subject to the constraints of the maximum transmit power of FBSs, the maximum cross-tier interference power of macrocell users, the minimum rates of femtocell users, and unit modulus of RISs. To facilitate the design, we develop an iterative block coordinate descent-based algorithm which exploits the semidefinite relaxation, the S-procedure, the successive convex approximation method, and the singular value decomposition method. Simulation results demonstrate the superiority of the proposed algorithm.

Energy efficiency (EE) is a crucial performance metric in wireless-powered backscatter communication networks (WP-BackComNets) for achieving a good tradeoff between data rates and the overall energy consumption, which however has not been sufficiently exploited by the existing works. In this paper, an EE-based maximization resource allocation (RA) problem is studied in a downlink orthogonal frequency division multiple access-based WP-BackComNet, where the circuit power consumption of the backscatter device, the minimum energy harvesting (EH) constraint, and the maximum transmit power constraint of the power station are considered. To deal with the non-convex problem, we firstly transform it into an equivalently subtractive form via Dinkelbach's method. Then, we apply a variable substitution approach to transform the non-convex problem into a convex one, where the closed-form solutions of the reflection coefficient, the transmit power, and the EH time are deduced by using Lagrange dual method. Simulation results demonstrate that the proposed algorithm can achieve better EE performance than other benchmark algorithms.

In this paper, deep learning is combined to learn the bit posterior probability (soft information) of iterative decoding for a bit-interleaved coded modulation with iterative decoding (BICM-ID) system. The deep neural network (DNN) is adopted to learn and replace multiple modules of the receiver, which can jointly deal with multiple problems and improve the efficiency of the whole system. The output of direct learning iteration reduces the computational cost of iteration to a certain extent. Simulation is carried out under Rayleigh channel and multiple modulation modes and results show that the proposed scheme without iteration is better than traditional BICM systems, and very close to traditional BICM-ID systems which needs iteration between demodulation and decoding.

Wireless-powered communication is a promising technique to provide convenient and perpetual energy for energy-constrained wireless devices. However, the uplink information transmission of wireless devices in wireless-powered communication networks relies on the harvested energy from the downlink-energy-transfer power station (PS). To tackle this issue, we propose a new intelligent reflecting surface (IRS)-assisted wireless-powered network architecture, where an IRS is deployed between a PS and multiple wireless-powered users to enhance the efficiency of energy harvesting. The total energy efficiency (EE) of all wireless-powered users is maximized by jointly optimizing the energy transfer matrix of the PS, the transmission time and power of users, and the phase shifts of the IRS. The formulated problem is non-convex and challenging to solve. Accordingly, the alternating optimization approach, Dinkelbach's method, and the variable-substitution approach are used to solve it. Simulation results verify the effectiveness of the proposed algorithm.

Non-orthogonal multiple access (NOMA)-based device-to-device (D2D) network has attracted widespread attention since it can address the problem of spectrum shortage in the next-generation communication networks. However, the robust resource allocation problem in this network has not been well investigated. In this paper, we aim for maximizing the energy efficiency (EE) of a NOMA-based D2D network with simultaneous wireless information and power transfer technique under imperfect channel state information. The considered problem is modeled as a non-convex optimization problem that considers the maximum tolerable outage probability of each D2D user (DU), the successive interference cancellation decoding order, and the maximum transmit power of base station and DUs, where the transmit power, power splitting factor, and resource block assignment factor are jointly optimized. Since the formulated mixed-integer fractional programming problem with outage probability constraints is non-convex and difficult to solve, we firstly transform it into a non-probabilistic problem through a relaxation approach, and then, transform it into a convex one by using the variable-substitution approach and Dinkelbach's method. Finally, an EE-based iterative algorithm is proposed to solve this intractable problem. Simulation results show that the proposed algorithm has a fast convergence and low outage probability.

Energy efficiency (EE) is a significant performance indicator in unmanned aerial vehicle (UAV)-assisted communication networks for providing a balance between power consumption minimization and transmission rate maximization. However, most of the current works focus on the transmission rate maximization under perfect channel state information (CSI) and exact coordinate information, which is too ideal in practical systems due to channel estimation errors and coordinate estimation errors. Thus, robust resource allocation algorithms with imperfect CSI and coordinate information are critically important to reduce users’ outages and improve system robustness. In this article, a robust EE maximization problem with channel uncertainties and coordinate uncertainties is formulated for an energy harvesting-based device-to-device (D2D) communication underlaying UAV-assisted network under some necessary constraints, which involve the outage probability constraints of ground terminals, the flight altitude constraint of the UAV, the minimum harvested energy constraints of D2D users, and the transmission time constraint. Both radio resource allocation and the flight altitude are jointly optimized based on the worst case approach. The considered nonconvex problem is transformed into a convex one by exploiting variable relaxation and variable substitution approaches. The Lagrange dual theory is used to derive the closed-form expressions of robust resource allocation. Simulation results demonstrate the effectiveness of the proposed algorithm by comparing it with the benchmark algorithms in terms of EE and robustness.

Secure information transmission and energy efficiency (EE) optimization are very important for simultaneous wireless information and power transfer (SWIPT)-aided heterogeneous networks. However, most of the existing works consider perfect channel state information (CSI) and linear energy harvesting (EH) models, which are too ideal in practical systems. In this article, we focus on the EE-based robust optimization with imperfect CSI and nonlinear EH models in a SWIPT-aided two-tier heterogeneous macro-femto network with multiple eavesdroppers. In particular, we formulate a robust beamforming problem by jointly optimizing the beamforming vectors of the macro base station (BS) and femto BSs, the power splitting (PS) factors of energy receivers, and the artificial noise vectors of BSs, under multiple constraints including the quality of service requirement of each user, the minimum harvested energy, the maximum transmit power, and the PS factor. Although the formulated robust optimization problem is nonconvex, an EE-based iterative algorithm is developed to obtain the solutions. Simulation results demonstrate the proposed algorithm is superior to other algorithms in terms of EE and security.

Fairness among different users and system robustness are key issues in the future communication network design. A robust max-min fairness energy efficiency (EE) maximization problem in a downlink non-orthogonal multiple access (NOMA) heterogeneous network is studied when channel state information and interference power are uncertain. A worst-case EE of the small cell is maximized by jointly optimizing the transmit power and cell association under the bounded channel uncertainty model, subject to constraints on the cross-tier interference power, maximum transmit power, and the minimum rate requirement of each small-cell user. The formulated robust max-min fairness EE problem is a mixed-integer and non-convex programming problem with infinite inequality constraints. An iterative resource allocation algorithm is designed based on the proposed power allocation and cell association scheme. Simulation results demonstrate the effectiveness of the proposed robust resource allocation scheme and its improvement over existing schemes.

Resource allocation (RA) is a key technique to guarantee the quality of service of users and maximize the system capacity in cognitive nonorthogonal multiple access (NOMA) networks. However, traditional RA approaches have been proposed under perfect channel state information (CSI) which is too ideal in practical systems due to the impact of link delays, quantization errors, etc. In this article, with imperfect CSI, a downlink robust RA algorithm is proposed for robust transmission to maximize the sum energy efficiency (EE) of secondary users (SUs) under channel uncertainties. Meanwhile, it protects the minimum data rates of SUs, satisfying the maximum transmission power constraints of base stations and the maximum interference temperature constraint of each primary user (PU). The formulated problem is nonconvex, thus challenging to solve. For delay-tolerant services, to deal with the intractability of the problem caused by outage probability constraints, the closed-form expressions of outage probabilities of SUs and PUs are derived under Gaussian CSI error models. For delay-sensitive services, the robust constraints with bounded uncertainty sets are transformed into convex ones. Based on successive convex approximation and the parameter transformation approach, the original problem is converted into a closed-form geometric programming problem solved by dual decomposition methods and subgradient methods. Additionally, users’ outage probabilities and the minimum required transmit power under the two modeling approaches are analyzed. Computational complexity and sensitivity analysis are provided. The simulation results show the proposed algorithm serves good robustness and EE.

In this paper, we studied the joint subchannel and power allocation problem to maximize energy efficiency (EE) for the multi-carrier non-orthogonal multiple access (MC-NOMA) systems. First, a matching algorithm is proposed to allocate the user in the subchannel. Then, the EE maximization problem is converted into a series of subproblems, which can be solved by the penalty function method. Next, the joint channel allocation and power allocation is proposed based on the Dinkelbach algorithm. Simulation results show that the proposed algorithm is superior to the traditional fractional power allocation (FTPA) scheme and the OFDMA scheme in terms of the EE.

In this paper, the interference-efficient based resource allocation for uplink transmission in an OFDMA-based cognitive radio network is studied. The interference efficiency (IE) is defined as the total data rate of secondary users (SUs) over sum interference power imposed on primary users (PUs). The objective is to maximize the total IE of SUs subject to transmit power constraints of SUs, subcarrier assignment constraint and the interference power threshold constraints of SUs. The original mixed integer fractional programming problem is converted into the convex one which is solved by using the convex optimization technique. Simulation results show the effectiveness of the proposed algorithm in terms of the interference to PUs and transmission efficiency.

Heterogeneous network (HetNet) with energy harvesting is a promising technique to provide perpetual power supplies and ubiquitous coverage as well as high data rate for next-generation wireless communications. In this article, we consider a robust power allocation and power splitting (PS) problem for downlink simultaneous wireless information and power transfer (SWIPT)-enabled HetNets. The robust energy-efficiency (EE) maximization problem of femtocell users (FUs) is formulated under the outage-probability interference power constraint of macrocell user (MU), the maximum allowable transmission power of FU, and the EE-based outage constraint of each FU. The originally fractional optimization problem with the probabilistic constraint is NP-hard and difficult to solve. Without knowing the distribution of uncertain parameters, a min–max probability machine approach isfirstintroduced to convert the semi-infinite optimization problem into a deterministic one which is transformed into a deterministic convex one by using the Dinkelbach method and the quadratic transformation approach. An iterative power allocation and PS scheme is obtained based on convex optimization methods. Finally, the effectiveness of the proposed algorithm is demonstrated by simulation results from the perspective of EE and robustness.

With the development of fifth generation wireless communication technology, how to improve system capacity and spectrum efficiency is a crucial problem in resource sharing of heterogeneous networks (HetNets). Most of existing resource allocation (RA) schemes in HetNets focus on perfect channel state information, however, exact channel information is difficult to obtain under link delay and stochastic channel condition. In order to resolve the RA issues under channel uncertainties, a robust RA algorithm is proposed to maximise the sum data rate of microcell users where the users are subjected to the individual transmission power constraint and the cross‐tier interference constraint of macrocell users. The multiuser RA problem in HetNets is formulated under the consideration of bounded channel gain uncertainties where both the cross‐tier channel and intra‐tier channel are simultaneously considered. The non‐linear optimisation problem is converted into a geometric programming problem that is solved by using Lagrange dual methods in a distributed way. Simulation results show that the proposed algorithm can well restrain the effect of channel uncertainty and achieve a good robustness.

How to improve system capacity and spectral efficiency is a key issue for next generation wireless communication. Heterogeneous network (HetNet) has been considered as a new promising technique for enhancing the quality of service and spectrum efficiency due to different radio access technology and network structures. However, conventional resource allocation algorithms in HetNets are achieved under the assumption of perfect parameter information which may be invalid in practical systems. In this paper, a robust rate maximization resource allocation problem for multiuser cognitive HetNets is formulated to flexibly use network resource and improve overall capacity where robust cross-tier interference constraint and maximum transmit power constraint of base station are simultaneously considered. The semi-infinite programming problem is converted into a geometric programming problem by using relaxation approaches. Simulation results show that the proposed algorithm can guarantee transmission performance of macrocell users and microcell users under channel uncertainties.

With the development of the fifth generation communication technology, how to improve system capacity and spectral efficiency is a key issue, which has attracted more and more attention from industry and academia. Heterogeneous network has been considered as a new promising technique for enhancing the quality of service, energy and spectrum efficiency as well as coverage of network due to different radio access technology (RAT) and different network structures. In this paper, the rate maximization resource allocation problem for multiuser cognitive heterogeneous networks is formulated to flexibly use network resource and improve the overall capacity, which simultaneously considers cross-tier interference constraint and maximum transmit power of cognitive microcell base station. The non-convex optimization problem is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results are given to show the performance of the proposed algorithm in terms of the achievable system capacity and the interference to the macrocell network.

This paper investigates power allocation problems for orthogonal frequency division multiplexing (OFDM)-based cognitive radio networks operating in licensed frequency bands. Considering imperfect spectrum sensing, a new power allocation algorithm is proposed to minimize the total interference introduced to primary user under a minimum capacity constraint of secondary user (SU) and a total transmit power constraint of the SU. The numerical results demonstrate that the proposed power allocation scheme can not only keep the rate requirement of SU under spectrum sensing errors by comparison with traditional method, but also fully make use of the limited spectrum resource.

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal‐to‐interference‐and‐noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst‐case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

Abstract One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

In cognitive radio networks, conventional power control algorithms (PCAs) based on instantaneous perfect channel gain may lead to performance degradation in practical systems, since channel uncertainties are inevitable because of quantisation errors and estimation errors. As a result, robustness of the algorithms becomes an important issue. However, traditional robust PCAs with probabilistic models require to perfectly know the distribution information of the estimation error (e.g. Gaussian distribution) which is difficult to obtain. Moreover, the distribution function of the actual error may not be Gaussian distribution. In this study, instead of using deterministic distribution model, a robust PCA based on a distribution‐free method is designed to minimise total transmit power of secondary users subject to probabilistic interference and signal to interference plus noise ratio constraints. Based on the minimax probability machine, the original problem is reformulated as a second order cone programming problem solved by interior‐point method. An adaptive estimation scheme is proposed to estimate the actual mean and covariance matrix of uncertain parameters. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm by comparing with the robust algorithms under worst‐case constraints and probabilistic constraints, respectively.