Wireless powered hybrid backscatter-active communications with hardware impairments

Wireless powered hybrid backscatter-active communication can full make use of the different tradeoff between power consumption and achievable rate of the active and backscatter communications, and thus achieving a better performance than wireless powered active or backscatter communications. In this paper, we design a throughput maximization-based resource allocation scheme for a wireless powered hybrid backscatter-active communication network, while considering the hardware impairments at all RF front ends of each transceiver. Towards this end, we formulate a problem by jointly optimizing the transmit power of the dedicated energy source, the time for pure energy harvesting, backscatter and active communications, the power reflection coefficient, and the transmit power of each IoT node during active communications. The formulated problem is non-convex and different to solve. Subsequently an iterative algorithm based on the block coordinated decent technology is proposed to address the above problem. Simulation results verify that our proposed iterative algorithm converges very fast and that the proposed scheme outperforms the baseline schemes in terms of the throughput.     

Secure efficiency maximization for UAV-assisted mobile edge computing networks

This paper considers an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, in which the intelligent reflecting surface (IRS) is applied to enhance the performance of the wireless transmission. The role of the UAV is twofold: (1) It is equipped with a MEC server and receives computing tasks from ground users and IRS at the same time; (2) It sends interference signals to counter the potential eavesdropper. Here, the UAV is working as a full duplex equipment, i.e., sending and receiving meanwhile. We comprehensively considered the flight speed constraint of the UAV, the total mission data constraint and the minimum security rate constraint of multiple users on the ground. The phase matrix constraints of IRS are also considered. Our system is dedicated to maximizing the efficiency of secure computing. The formulated problem is highly non-convex, we consider to propose an alternative optimization algorithm. The simulation results show that the proposed scheme not only achieves higher safe computing efficiency, but also has better performance in terms of energy consumption and security rate.     

AN-aided beamforming for IRS-assisted SWIPT systems

This paper studies artificial noise (AN)-aided beamforming design in an intelligent reflecting surface (IRS)-assisted system empowered by simultaneous wireless information and power transfer (SWIPT) technique. Multiple power splitting (PS) single-antenna receivers simultaneously receive information and energy from a multi-antenna base station (BS). Although all users are legitimate, in each transmission interval only one receiver is authorized to receive information and the others are only allowed to harvest power which are considered as unauthorized receivers (URs). To prevent information decoding by URs, AN signal is transmitted from the BS. We adopt a non-linear model for energy harvesting. In the optimization problem, we minimize the total transmit power, and for this purpose, we utilize an alternating optimization (AO) algorithm. For the non-convex rank-one constraint for IRS phase shifts, we utilize a sequential rank-one constraint relaxation (SROCR) algorithm. In addition to single antenna URs scenario, we investigate multi-antenna URs scenario and evaluate their performance. Simulation results validate the effectiveness of using IRS.     

3D beamforming in Intelligent Reflecting Surface (IRS)-assisted multi-user cognitive radio networks

In this paper, we consider a multi-user cognitive radio network (CRN) equipped with an intelligent reflecting surface (IRS). We examine the network performance by evaluating the fairness of the secondary system, which is satisfying the minimum required signal to interference and noise ratio (SINR) for each secondary user (SU). The minimum SINR of the SUs is maximized by joint optimization of the beamforming vector and three-dimensional beamforming (3DBF) angles at the secondary base station (SBS) and also the phase shifts of the IRS elements. This optimization problem is highly non-convex. To solve this problem, we utilize Dinkelbach’s algorithm along with an alternating optimization (AO) approach to achieve some sub-problems. Accordingly, by further applying a semi-definite relaxation method, we convert these sub-problems to equivalent convex forms and find a solution. Furthermore, analytically we propose an algorithm for optimizing 3DBF angles at the SBS. Through numerical results, the improvement of the sum SINR of the secondary system using the proposed method is illustrated. Moreover, it is shown that as the number of reflecting elements of IRS increases, the sum SINR significantly augments while satisfying fairness. Also, the convergence of the proposed algorithm is verified utilizing numerical results.     

Particle swarm optimization based network selection in heterogeneous wireless environment

Deployment of heterogeneous wireless networks is spreading throughout the world as users want to be connected anytime, anywhere, and anyhow. Meanwhile, users are increasingly interested in multimedia applications such as audio, video streaming and Voice over IP (VoIP), which require strict Quality of Service (QoS) support. Provisioning of Always Best Connected (ABC) network with such constraints is a challenging task. Considering the availability of various access technologies, it is difficult for a network operator to find reliable criteria to select the best network that ensures user satisfaction while reducing multiple network selection. Designing an efficient Network selection algorithm, in this type of environment, is an important research problem. In this paper, we propose a novel network selection algorithm utilizing signal strength, available bit rate, signal to noise ratio, achievable throughput, bit error rate and outage probability metrics as criteria for network selection. The selection metrics are combined with PSO for relative dynamic weight optimization. The proposed algorithm is implemented in a typical heterogeneous environment of EDGE (2.5G) and UMTS (3G). Switching rate of the user between available networks has been used as the performance metric. Moreover, a utility function is used to maintain desired QoS during transition between networks, which is measured in terms of the throughput. It is shown here that PSO based approach yields optimal network selection in heterogeneous wireless environment.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

Energy efficiency maximization for IRS-assisted NOMA networks

In this paper, we consider a non-orthogonal multiple access (NOMA) system assisted by intelligent reflecting surface (IRS). As an emerging technology that has received widespread attention, IRS can reconfigure the wireless channel environment by adjusting the relationship between the incident angle and the exit angle, thereby improving system performance. Our goal is to use the flexible assistance of IRS to achieve the maximum energy efficiency of the NOMA system. Our design objects are the beam vector design of the base station and the phase matrix design of the IRS. The original problem is highly non-convex. We consider using the block coordinate descent method to design the phase matrix and beam vector separately. Simulation results show that our proposed scheme has better performance than traditional OMA and systems without IRS assistance.     

一种基于授权信道特性的认知无线电频谱检测算法

针对认知无线电系统中频谱检测的频率直接影响系统容量以及与授权用户产生冲突的概率问题,分析了授权用户频谱使用的特性, 对授权用户行为进行统计建模, 提出一种自适应频谱检测算法. 引入控制因子, 在保证认知无线电系统稳定性的约束下, 自适应调整频谱感知的频率从而提高频谱利用率并减小系统冲突概率和检测开销, 进而降低了系统的能量消耗. 仿真结果表明, 该算法在保证不对授权用户产生干扰和一定的系统稳定性条件下, 有效地提高了系统的容量,并且具有良好的实用性和灵活性. 关键词： 认知无线电 自适应频谱检测 绿色通信 最大似然     

Transcutaneous photocoupler for transmission of biological signals

A compact, photocouplerlike system is presented for wireless transcutaneous transmission of biological signals. The system consists of an optically powered near-infrared light transmitter, a receiver for the signals from it, and a near-infrared light source with which to irradiate it. The transmitter, which is implanted under the skin, is transcutaneously coupled with the receiver and the light source, which are combined and placed on the skin. The transmitter, which is powered transcutaneously by the light source, then amplifies a signal input to it and sends intensity-modulated light transcutaneously to the receiver. With this system, electromyograms and neuronal firing patterns of live rats have been successfully recorded.     

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).     

Computation rate maximization for IRS-enhanced dynamic spectrum access assisted MEC system

Intelligent reflecting surface (IRS)-enhanced dynamic spectrum access (DSA) is a promising technology to enhance the performance of the mobile edge computing (MEC) system. In this paper, we consider the integration of the IRS enhanced DSA technology to a MEC system, and study the pertinent joint optimization of the phase shift coefficients of the IRS, the transmission powers, the central processing unit (CPU) frequencies, as well as the task offloading time allocations of the secondary users (SUs) to maximize the average computation bits of the SUs. Due to the non-convexity, the formulated problem is difficult to solve. In order to tackle this difficulty, we decompose the optimization problem into tractable subproblems and propose an alternating optimization algorithm to optimize the optimization variables in an iterative fashion. Numerical results are provided to show the effectiveness and the correctness of the proposed algorithm.     

Latency minimization for IRS-assisted mobile edge computing networks

In this paper, we consider the latency minimization problem via designing intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) networks. For the scene when local users cannot complete all computing tasks independently, a common solution is transferring tasks to cloud servers. We consider that the MEC system contains multiple independent users, and each user sends task data to the base station in a partially offloaded manner. Our goal is to minimize the maximum latency for all users. The original problem is strongly non-convex, which caused difficulty to solve. We first introduce a new variable to transform the max–min problem into an alternative minimization problem, and then solve each optimization variable separately by the block coordinate descent method. Finally, our simulation experiments demonstrate that our proposed scheme obtain better performance with respect to other existing schemes.     

Joint optimization of component carrier selection and resource allocation in 5G carrier aggregation system

In this paper, we consider joint optimization of Component Carrier (CC) selection and resource allocation in 5G Carrier Aggregation (CA) system. Firstly, the upper-bound system throughput with determined number of CCs is derived and it is proved by using graph theory that the throughput optimization problem is NP hard. Then we propose a greedy based algorithm to solve this problem and prove that the proposed algorithm can achieve at least 1/2 of the optimal performance in the worst case. At last, we evaluate the throughput and computational complexity performance through a variety of simulations. Simulation results show that the proposed algorithm can obtain better performance comparing with existing schemes while keeping the computation complexity at an acceptable level.     

New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks

This paper considers a multi-user wireless communication network supported by a multiple-antenna base station (BS), where the users who are located sufficiently close to the BS employ wireless energy harvesting (EH) to replenish their energy needs. The objective of this work is to design an efficient beamforming to maximize the minimum throughput among all the information users (IUs), subject to EH constraints. In this regard, transmit time-switching approach is employed, where energy and information are transmitted over different fractions of a time-slot. To achieve efficient EH, a conjugate beamforming (matched filtering) is applied. To design efficient information beamforming for max–min throughput optimization, conventional zero-forcing (ZF) beamforming can be adopted, however, it will not suppress multi-user interference if the number of users is greater than the number of antennas at the BS. To this end, different from the existing works which employ regularized zero-forcing (RZF) beamforming, this work proposes a new generalized zero-forcing (GZF) beamforming, which promises better max–min throughput compared to that achieved by the RZF beamforming. A new path-following algorithm is developed to achieve max–min throughput optimization by GZF beamforming, which is based on a simple convex quadratic program over each iteration.     

Spectral efficiency maximization for IRS-assisted wireless communication in cognitive radio networks

In this paper, we consider the Intelligent reflecting surface (IRS)-assisted cognitive radio (CR) network, in which the IRS is applied to improve the spectral efficiency of secondary users. In specific, the advantages of applying IRS is double: it can not only improve the transmission rate of the secondary users, but also reduce the interference from the secondary users to the primary users, which allow the secondary users to increase the transmission power correspondingly. The original problem is formulated by simultaneously consider the maximum power limitation of the secondary users and the Quality of Service (QoS) requirement of the primary users, which expressed by the interference temperature (IT). The formulated problem is non-convex and difficult to solve directly. We apply the alternating optimization (AO) algorithm to split the original problem into two sub-problems. For the first sub-problem, we transform it into a convex problem and for the second sub-problem, we use the successive convex approximation method to obtain the corresponding results. The simulation experiments show that the performance of our proposed scheme is better than existing schemes.     

Array design for mm-wave UAV-assisted cooperative communications with simultaneous wireless information and power transmission

High performance UAV-assisted communications system using simultaneous wireless information and power transmission (SWIPT) in mm-wave band is presented. UAV is a moving relay powered from a ground source through a power-splitting mechanism. In mm-wave band we utilize antenna array to increase the antenna gain while keeping array size small and practical. The radiation pattern of the UAV antenna is continuously adjusted to peak towards the source and destination. Two array geometries, a line and a cross, are designed for UAV antenna. We achieve near optimal pattern, utilizing innovative low power switches instead of phase shifters which are high power consuming components and using them here defies the purpose. We maximize the end-to-end cooperative throughput by optimizing the UAV power profile, power-splitting ratio profile, antenna weights (0, 1), and UAV trajectory for amplify-and-forward (AF) protocol. We consider two cases. Case1: UAV transmits and receives data simultaneously along two predefined trajectories. The antenna weights for line and cross arrays are optimized utilizing genetic algorithm. The power profile and, power-splitting ratio profile are also optimized using the penalty method. Case2: UAV accumulates the data and power along an optimal trajectory until it reaches the vicinity of target, when it transmits data at high bit rate. Here we define the optimization of parameters mentioned in Case1, while at an optimal point along the trajectory, as sub-problem1, and finding the next optimal point as sub-problem2. Sub-problem1 is solved using the genetic algorithm and dual decomposition method. Sub-problem2 is then solved using successive concave optimization. The overall problem, i.e. cooperative throughput, is solved by reciprocal iteration over the two sub problems. The simulation results show the proposed mm-wave band cross array antenna and switches can overcome the high frequency propagation losses, hence, achieving higher power harvest and data rates. The achieved higher data throughput outperforms the conventional single antenna low frequency systems.     

Performance analysis of wireless powered sensor network with opportunistic scheduling over generalized κ−μ shadowed fading channels

This paper investigates the performance of a multi-node wireless powered sensor network (WPSN) with an opportunistic scheduling scheme over $\kappa -\mu$ shadowed fading channels. The system assumes that all the sensor nodes (SNs) are energy constrained and harvest energy from a hybrid access point (HAP) in the downlink. In contrast, the node with the best end-to-end instantaneous signal-to-noise ratio (SNR) is scheduled for information transmission to HAP in the uplink. For the underlying system model, approximate closed-form analytical expressions is developed with the help of the moment matching method, which is then used to evaluate the system performances such as outage probability, effective throughput, and average bit error rate (BER). In addition, we also perform an asymptotic analysis by assuming that the system operates at a high SNR region, which gives us valuable insights about the diversity order and coding gain. The accuracy of the analysis is further confirmed with Monte-Carlo simulations, which validate the correctness of the theoretical analysis.     

Fairness optimization in IRS-assisted MEC computational offloading

Nowadays, more and more multimedia services are supported by Mobile Edge Computing (MEC). However, the instability of the wireless environment brings a lot of uncertainty to the computational offloading. Additionally, intelligent reflecting surface (IRS) is considered as a potential technology to enhance Quality of Service (QoS). Therefore, in this paper, we establish a framework for IRS-assisted MEC computational offloading to solve this problem and take fairness optimization as a key point involving communication and computing resources. Minimize user consumption by optimizing bandwidth allocation, task offloading ratio, edge computing resources, transmission power and IRS phase shifts. Firstly, we decompose the problem into three aspects, such as bandwidth allocation, computing resource allocation, transmission power and IRS phase shifts. Then, an alternative optimization algorithm is proposed to find the optimum solution and its convergence is proved. Secondly, since the optimization problem on transmission power and IRS phase shifts is non-convex, we propose Riemann gradient descent (R-SGD) algorithm to solve it. Finally, numerical results show that our proposed algorithm performs better than other algorithms and achieves a superiority in the framework.     

Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.     

Joint terminal-AP association and power allocation for NOMA-enabled space–air–ground integrated networks

This paper considers a space–air–ground integrated network (SAGIN) to provide network access services for aerial and terrestrial terminals. The non-orthogonal multiple access (NOMA) is used for improving spectral efficiency in the uplink transmission between terminals and access points (APs) in SAGIN. A sum rate maximization optimization problem is formulated by optimizing terminal-AP association and power allocation, while simultaneously satisfying the constraints of transmit power, network coverage characteristics, and quality-of-service (QoS) requirements of both aerial and terrestrial terminals. To deal with the formulated mixed integer nonlinear programming (MINLP) optimization problem, we first decouple it into separated terminal-AP association and power allocation problems. Then, we adopt the Q-learning algorithm to solve the terminal-AP association subproblem. Based on the obtained terminal-AP association solution, an iterative power allocation algorithm is developed by exploiting the Lagrange dual method. Moreover, the computational complexity of the proposed algorithm is further analyzed. Simulation results demonstrate that, compared with other schemes, our proposed algorithm can achieves a better performance in terms of the achievable sum rate, average achievable rate, and outage probability.