The combination of simultaneous wireless information and power transfer (SWIPT) and non-orthogonal multiple access (NOMA) is a potential solution to improve spectral efficiency (SE) and energy efficiency (EE) of the upcoming fifth generation (5G) networks, especially in order to support the functionality of the Internet of things (IoT) and the massive machine-type communications (mMTC) scenarios. In this paper, we investigate joint power allocation and time switching (TS) control for EE optimization in a TS-based SWIPT NOMA system. Our aim is to optimize the EE of the system whilst satisfying the constraints on maximum transmit power budget, minimum data rate and minimum harvested energy per-terminal. The considered EE optimization problem is neither linear nor convex involving joint optimization of power allocation and time switching factors, and thus is extremely difficult to solve directly. In order to tackle this problem, we develop a dual-layer algorithm where Dinkelbach method is employed both in the inner-layer to optimize the power allocation and in the outer-layer to control the time switching assignment. Furthermore, a simplified but practical special case with equal time switching factors in all terminals is considered. Numerical results validate the theoretical findings and demonstrate that significant performance gain over orthogonal multiple access (OMA) scheme in terms of EE can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

This paper proposes a novel secure intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) system, where the UAV sends confidential messages to a legitimate receiver, in the presence of a passive eavesdropper. We aim to maximize the secrecy rate by jointly designing the trajectory, the power control of UAV and the phase shifters of the IRS from the physical layer security perspective. Because the formulated problem is non-convex and intractable, we propose an iterative algorithm based on successive convex approximation (SCA) to solve this problem. Simulation results validate the effectiveness of the proposed algorithm and show that the secrecy rate is significantly improved with the assistance of the IRS.

This letter proposes a novel hybrid relay and Intelligent Reflecting Surface (IRS) assisted system for future wireless networks. We demonstrate that for practical scenarios where the amount of radiated power and/or the number of reflecting elements are/is limited, the performance of an IRS-supported system can be significantly enhanced by utilizing a simple Decode-and-Forward (DF) relay. Tight upper bounds for the ergodic capacity are derived for the proposed scheme under different channel environments, and shown to closely match Monte-Carlo simulations.

Due to hardware limitations, the phase shifts of the reflecting elements of reconfigurable intelligent surfaces (RISs) need to be quantized into discrete values. This letter aims to unveil the minimum required number of phase quantization levels L in order to achieve the full diversity order in RIS-assisted wireless communication systems. With the aid of an upper bound of the outage probability, we first prove that the full diversity order is achievable provided that L is not less than three. If L = 2, on the other hand, we prove that the achievable diversity order cannot exceed (N + 1)/2, where N is the number of reflecting elements. This is obtained with the aid of a lower bound of the outage probability. Therefore, we prove that the minimum required value of L for achieving the full diversity order is L = 3. Simulation results verify the theoretical analysis and the impact of phase quantization levels on RIS-assisted communication systems.

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-based modulation scheme, named RIS-aided receive quadrature reflecting modulation (RIS-RQRM), by resorting to the concept of spatial modulation. In RIS-RQRM, the whole RIS is virtually partitioned into two halves to create signals with only in-phase (I-) and quadrature (Q-) components, respectively, and each half forms a beam to a receive antenna whose index carries the bit information. Furthermore, we design a low-complexity and non-coherent detector for RIS-RQRM, which measures the maximum power and polarities of the I- and Q- components of received signals. Approximate bit error rate (BER) expressions are derived for RIS-RQRM over Rician fading channels. Simulation results show that RIS-RQRM outperforms the existing counterparts without I/Q index modulation in terms of BER in the low signal-to-noise ratio region.