In this paper, we propose a downlink intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) for millimeter-wave (mmWave) massive MIMO with lens antenna array, i.e., IRS-aided mmWave beamspace NOMA, where the single-antenna users without direct-link but connected to the base station (BS) with the aid of the IRS are grouped as one NOMA group. Considering the power leakage problem in beamspace channel and the per-antenna power constraint, we propose two multi-beam selection strategies for the BS-IRS link under two channel models, i.e., 2-dimension (2D) channel model and 3-dimension (3D) channel model, respectively, where two corresponding RF chain configuration strategies are designed, respectively. Then, we formulate and solve the optimization problem for maximizing the weighted sum rate by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, where we propose the alternating optimization (AO) method to solve the above joint optimization problem. Especially, different from the stochastic method, based on the beam-splitting technique, we propose the method to initialize the feasible solution for the proposed AO method, where the conditional transmit power minimization problem is formulated and solved. Through simulations, the weighted sum rate performance of the proposed IRS-aided mmWave beamspace NOMA is verified.

Massive multi-input multi-output (MIMO) is envisioned as a key technology for the emerging fifth generation of communication networks (5G). However, considering the energy consumption of the large number of radio frequency (RF) chains, massive MIMO poses a problem to energy efficiency (EE) requirement of 5G. In this paper, we propose an energy-efficient power allocation method for millimeter-wave (mmWave) beamspace MIMO non-orthogonal multiple access (NOMA) systems, where there may be multiple users in each selected beam. First, according to the beam selection (BS) results, we get the precoding matrix through zero-forcing (ZF) beamforming method. Second, we formulate the energy efficiency (EE) maximization optimization problem as a fractional programming. Through sequential convex approximation (SCA) and second-order cone (SOC) transformation, the original optimization problem can be transformed to a convex optimization problem. By using iterative optimization algorithm, we can get the power allocation results. Then, we analyze the convergence of our proposed iterative optimization method and get that the solution in each iteration is a suboptimal solution to the original non-convex optimization problem. Simulation results show that the proposed energy-efficient power allocation scheme has better EE performance comparing with the conventional methods when the transmitted power exceeds the power threshold.

Different from the conventional single-beam beamspace non-orthogonal multiple access (NOMA) scheme, where the users only within the same analog beam can be served by the NOMA technique, in this paper, we propose a multi-beam NOMA scheme for the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with lens antenna array communication system. The proposed scheme can break the single-beam constraint and perform NOMA transmission within the different analog beams. Then, we design the power allocation for minimizing the total power consumption, which mainly includes two stages. In the first stage, after performing beam scanning and beam selection, we design the game-theory based user grouping method for minimizing the conditional total power consumption. In the second stage, with the achieved user grouping solution and the corresponding beamspace channel state information (CSI), we formulate and solve the power allocation problem for minimizing the total power consumption. In addition, given the user grouping solution for minimizing the total power consumption, for evaluating the sum rate performance of the proposed multi-beam beamspace MIMO-NOMA scheme, we formulate and solve the power allocation optimization problem for maximizing the sum rate. In simulations, compared with the single-beam beamspace MIMO-NOMA scheme, given the user grouping solution, the proposed scheme has inferior sum rate performance when the power budget is enough high. However, at the cost of a little higher transmit power consumption, the proposed scheme has lower total power consumption and can simultaneously serve more users with the same number of RF chains.

The active intelligent reflecting surface (IRS) can amplify the incident signal besides reflecting, thus alleviating the double-fading effect of the cascaded link. However, if the interference caused by the cascaded link cannot be effectively cancelled, the alleviated effect may result in increased interference among users, thus limiting the performance gain brought by the active IRS. To this end, an active IRS aided rate-splitting multiple access (RSMA) scheme is proposed in this paper for the millimeter-wave hybrid antenna array. In the proposed scheme, a user grouping method is designed to circumvent the narrow beamwidth constraint, and the 2-layer rate-splitting (RS) strategy applicable to the user grouping result and the corresponding successive interference cancellation are designed to cancel interference. To evaluate the max-min fairness (MMF) performance of the proposed scheme, an MMF optimization problem (OP) is formulated, where the digital precoder, the RS vector, and the reflecting vector are jointly optimized. An alternating optimization (AO) algorithm with a novel AO strategy is proposed to solve the MMF OP, where the RS vector is optimized in two subproblems. An initialization method is designed specially to start the AO algorithm. Simulation results illustrate that the proposed scheme can more fully exploit the active IRS, and although the active IRS consumes additional power, it can bring energy efficiency gain to system.

The security of user-centric cell-free massive multiple-input multiple-output (UC-CF mMIMO) system in the presence of multiple collusive eavesdroppers (Eves) is investigated in this letter. The access points (APs) share the estimated channel state informations (CSIs) of serving users to central processing unit (CPU). Utilizing the received partial CSIs, the CPU optimize precoding via formulating a secracy rate maximization problem under minimum rate requirements of users and power constraints of APs. The formulated problem is non-convex, and the optimum solution is acquired by iteratively solving a sequence of second-order cone programming problems. The numerical simulations present the comparison of secrecy performance achieved in different AP selection method of UC-CF system, and CF system.

In multi-access edge computing, services are cached from cloud servers to the edge base station providing low-latency services. Horizontal Collaboration (HC) between Base Stations (BSs) can alleviate the problem of resource limitation in edge base stations. However, the initiative of cooperation between base stations and the interests of base stations themselves are not guaranteed. In this paper, we study the Joint Service Caching and Task Offloading decision Problem (JSCTOP). To solve this problem, we propose a Two-Stage Optimization Framework (TSOF) based on cooperative community idea to maximize the benefit of each base station. In the first stage, a Collaborative Community Mechanism (CCM) is designed based on the Dissimilarity of Service Types (DST) to enhance the collaborative capability of BSs. In the second stage, an algorithm for service caching and task decision to update and replace the Service Types (STs) of BSs. We also design an algorithm for service pricing to incentivize BSs. In this algorithm, BSs are able to profit from providing services to users and also profit from assisting other base stations. Extensive simulation results show that TSOF outperforms its counterparts in average delay, total energy, total benefit, and community.