Backscatter communication (BackCom) has gained increasing attention due to its low power consumption and low cost. However, its performance is severely degraded by the near-far effect due to the limited backscattering power at the tag side. In this paper, we consider two effective methods to solve the above problem by using the reconfigurable intelligent surface (RIS) and the relay. The objective is to maximize the received signal-to-noise ratio (SNR) subject to the transmit and circuit power control. To gain insight into both schemes, we derive a closed-form expression for the coverage radius of RIS-aided BackCom. Furthermore, considering the relay as a competing technology to the RIS, we compare their performance in terms of transmission performance, required transmit power, and quantify the number of reflecting elements needed for the RIS to outperform the relay. Simulation results demonstrate the RIS-aided BackCom with a sufficiently large number of elements achieves a superior performance, offers greater deployment flexibility, and is suitable for high self-interference levels.

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.

Reconfigurable intelligent surface (RIS), a recently emerging technology in wireless communications, has been gradually attracting widespread attention. Generally, a high array gain can be achieved by increasing the number of reflecting elements. However, the number of elements is limited by cost, overhead, deployment environments, power consumption, etc. In particular, the cost and power consumption may be high when a large number of elements are deployed, which results in a low energy efficiency even if there is an increase in the spectral efficiency. On the other hand, an increase in the number of elements does not necessarily increase the performance since it significantly increases the delivery overhead and may compress the data transmission time. Thus, number configuration for the RIS-aided systems appears to be particularly important. To demonstrate the advantages of the number configuration for the RIS-aided systems, in this article, we first introduce the overview of the RIS that is profitable for number configuration. Then, several design frameworks of number configurations are proposed and discussed, such as hybrid RIS, element on-off control, hybrid phase shift, phase delivery, etc., followed by numerical results to demonstrate the achieved benefits of dynamic number configuration. Furthermore, future directions for number configuration applications are presented, including functionality extensions and framework extensions. Finally, the open issues of realizing number configuration for the RIS-aided systems are outlined and elaborated upon.

Mobile edge computing (MEC) has been regarded as a promising technique to support latency-sensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy-efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost via jointly optimizing the transmission time, the transmit power, the receive beamforming vector, the phase-shift matrix, the mode-switching factor, the amplification factor, the offloading ratio factor, and the computation ability of the user, where the constraints of the maximum energy of users, the maximum power of the RIS, the minimum computation tasks, the transmission time, the offloading ratio factor, the mode-switching factor, the unit moduli of passive units, and the computation ability are taken into account. Considering the complexity of the aforementioned problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

Cognitive radio has been suggested as a solution to address the shortage of accessible spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. Nevertheless, the sensing performance is rather inadequate owing to the diminished sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, applying reconfigurable intelligent surfaces (RIS) for spectrum sensing can efficiently address the aforementioned problems. However, the passive RIS may experience the “double fading” effect, seriously limiting the effectiveness of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. In this paper, we utilize the passive and active RIS to further enhance detection probability and subsequently develop two different problems for both the passive and active RIS to achieve the detection probability maximization. Considering the complexity of the above problems, we design a one-stage optimization algorithm featuring inner approximation and a two-stage optimization algorithm that employs the bisection method to derive corresponding solutions, and further establish the upper bound and lower bound of the detection probability by employing the Rayleigh quotient. Moreover, we separately explore how many reflecting elements are needed for passive RIS and active RIS and investigate the detection performance comparison of the two types (passive and active) of RIS. Simulation results show that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes.

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.

Reconfigurable intelligent surface (RIS) has been regarded as a promising technique due to its high array gain, low cost, and low power. However, the traditional passive RIS suffers from the “double fading” effect, which has become a major bottleneck in restricting the performance of passive RIS-aided communications. Fortunately, active RIS can alleviate this problem by adjusting the phase shift and amplifying the received signal simultaneously. Nevertheless, a high beamforming gain often requires a large number of reflecting elements, which leads to non-negligible power consumption, especially for the active RIS. Thus, one challenge is how to improve the scalability of the RIS and the energy efficiency. Different from the existing works where all reflecting elements are activated, we propose a novel element on-off mechanism where reflecting elements can be flexibly activated and deactivated. To achieve a tradeoff between the transmission rate and energy consumption, two different optimization problems for passive RIS and active RIS are formulated by maximizing the total energy efficiency, where the constraints of the maximum power of users and the RIS, the minimum transmission rate, the element on-off factor, and the unit moduli of passive elements are taken into account. In light of the intractability of the formulated problems, we develop two different alternating optimization-based iterative algorithms by combining quadratic transform, variable substitution, and the successive convex approximation method to obtain sub-optimal solutions. Furthermore, in order to gain more insight into problems, we consider special cases involving transmission rate maximization problems for given the same total power budget, and respectively analyze the number configuration for passive RIS and active RIS. Simulation results verify that the proposed algorithms outperform existing algorithms, and reflecting elements under the proposed algorithms can be flexibly activated and deactivated.

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.

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.

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.