In this paper, a cooperative non-orthogonal multiple access (NOMA) mobile edge computing (MEC) network aided by reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) is considered, where the transmitters offload parts of their computing tasks to the MEC server via a relay and an RIS for enhancing the transmission environment. The goal is to minimize the task delay and energy consumption for the considered networks by jointly designing the RIS's reflection beamforming vector, the power splitting (PS) ratio, the power allocation factors of the relay, as well as the offloading transmit power and the offloading task ratio. To solve the proposed problems, an iterative optimization algorithm based on the successive convex approximation (SCA) method and the penalty method is proposed to decompose the formulated problems into two sub-problems. In particular, we jointly optimize the PS ratio, the power allocation coefficients and the edge computing resource based on the SCA technology, and then the penalty method is used to optimize the RIS's reflecting beamforming vector. Simulation results illustrate that the proposed NOMA-MEC networks aided by RIS and SWIPT are superior to the conventional orthogonal multiple access MEC networks with SWIPT aided by RIS in terms of task delay and energy consumption.

In this paper, we investigate secure transmission in active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems. The dual-function base station communicates with the legitimate user and detects the sensing target simultaneously via active RIS, while the communication signal is intercepted by a potential eavesdropper (Eve). To achieve secure transmission, a novel successive interference cancellation scheme is proposed, where the sensing signal can disrupt the potential eavesdropping while keeping the legitimate user link intact. Then, the secure rate maximization problem is proposed, where the active beamforming of the RIS, the legitimate user's transmit beamforming, the Eve's transmit beamforming, and the radar receive beamforming are jointly optimized. However, the secure rate expression is non-convex, an iteration optimization algorithm based on majorization-minimization method and semi-definite programming are proposed to decompose the original problem into three sub-problems. Simulation results illustrate that the secrecy performance of the proposed scheme outperforms the passive RIS-assisted ISAC systems and the traditional cellular networks aided by active RIS.

Ultra-dense HetNets (UDN)-based Millimeter-Wave (mmWave) massive MIMO is considered a promising technology for 5th generation (5G) wireless communications systems since it can offer massively available bandwidth and improve energy efficiency (EE) substantially. However, in UDN, the power consumption of the system increases sharply with the increase of network density. In this paper, we investigate the optimization of the EE in the mmWave massive MIMO systems with UDN. To develop the functions of massive MIMO, we first propose a system model where the massive MIMO harvests electromagnetic energy from the environment employing simultaneous wireless information and power transfer (SWIPT) technology, implemented at the base station (BS). Then, the EE optimization problem is formulated for 5G mmWave massive MIMO systems within the UDN. Considering the nonconcave feature of the objective function, an iterative EE algorithm is developed, based on Dinkelbach method. To utilize the role of coordinated multi-point transmission and reception (CoMP) for improving the EE, a coordinated user(UE)-BS association algorithm-based CoMP with maximum energy efficiency (MaxEE) is proposed. The simulation results demonstrate that the proposed algorithm has a substantially faster convergence rate and is very effective, compared with existing methods.

This paper considers the application of non-orthogonal multiple access (NOMA) into cooperative cognitive radio (CR) networks with simultaneous wireless information and power transfer (SWIPT). For NOMA in cooperative CR networks with SWIPT, the cognitive relay harvests the transmission power from the secondary transmitter with power splitting scheme, while the fixed power allocation scheme is used for the NOMA protocol. The closed-form analytical expression of the overall outage probability for the proposed networks is derived, as well as its diversity order at high signal-to-noise ratio (SNR) region is investigated. Furthermore, compared to OMA in cooperative CR networks with SWIPT, the proposed scheme can always achieve the same diversity order, but lower overall outage performance. Compared with NOMA in cooperative CR networks using its own battery for transmission, the SWIPT NOMA in cooperative CR networks will lead to losing a little of the overall outage performance, but without losing the diversity order.

We investigate the activity detection and channel estimation issues for cell-free Internet of Things (IoT) networks with massive random access. In each time slot, only partial devices are active and communicate with neighboring access points (APs) using non-orthogonal random pilot sequences. Different from the centralized processing in cellular networks, the activity detection and channel estimation in cell-free IoT is more challenging due to the distributed and user-centric architecture. We propose a two-stage approach to detect the random activities of devices and estimate their channel states. In the first stage, the activity of each device is jointly detected by its adjacent APs based on the vector approximate message passing (Vector AMP) algorithm. In the second stage, each AP re-estimates the channel using the linear minimum mean square error (LMMSE) method based on the detected activities to improve the channel estimation accuracy. We derive closed-form expressions for the activity detection error probability and the mean-squared channel estimation errors for a typical device. Finally, we analyze the performance of the entire cell-free IoT network in terms of coverage probability. Simulation results validate the derived closed-form expressions and show that the cell-free IoT significantly outperforms the collocated massive MIMO and small-cell schemes in terms of coverage probability.

In this article, we investigate the energy efficiency (EE) of reconfigurable intelligent surfaces (RISs) aided cell-free ultra-dense HetNets (CFUDN). To maximize the EE of CFUDN, users (UEs) association and clustering, RISs subsurface (SSF) associations are carefully designed. Then, the phase shift matrix of RISs and transmission power of base stations (BSs) are jointly optimized. Due to the non-convexity and high complexity of the formulated problem, this jointly optimized problem is usually difficult to solve. At present, the most commonly adopted method to deal with joint optimization problems is the block coordinate descent (BCD) algorithm based on an alternative optimization framework. However, as we all know, the BCD algorithm has some degree of performance loss due to the alternate optimization of variables. To overcome this challenging issue, a novel joint optimization framework based on Riemannian product manifolds (RPM) is proposed, in which the phase shift matrix of RISs and transmission power of BSs can be simultaneously optimized rather than alternating ways. The simulation results and computational complexity analysis demonstrate that the joint optimization framework based on the RPM exhibits superior performance and lower complexity compared to the joint optimization algorithm based on the BCD.