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.

In this paper, we consider improving the secure performance of multiple-input single-output visible light communication channel in the presence of multiple eavesdroppers with multiple photodiodes. Our goal is to design an optimal artificial-noise (AN) aided transmission strategy to maximize the achievable secrecy rate subject to both sum power constraint and peak amplitude constraint. We consider a joint optimization of the transmit covariance and AN covariance for the non-convex secrecy rate maximization (SRM) problem. In order to solve it, the SRM problem is transformed into a series of single-variable semidefinite programming (SDP) problems without losing any optimality, and a one-dimensional search based algorithm is proposed to handle the converted problem, with polynomial complexity. By exploiting Karush-Kuhn-Tucker conditions of the problem, beamforming is found to be optimal for the confidential information transmission. Simulation results show the superior performance of the proposed AN-aided method compared with two other AN-aided methods and no AN-aided method.

Accurate channel state information (CSI) acquisition is indispensable for mmWave massive MIMO systems to perform sophisticated signal processing. The existing channel estimation methods such as beam training and compressed sensing either have low accuracy or require considerably high training overhead. To achieve a fast yet accurate channel estimation, we propose a parallel hierarchical multi-beam search method in this paper. Unlike the existing beam search methods, which search multipath components (MPCs) one by one, the proposed method searches all the MPCs simultaneously by scanning all the beam pairs with MPCs in each layer of codebook in parallel. Therefore, the beam search of different MPCs does not interfere with each other. To improve the estimation accuracy and reduce the beam training overhead, the length of each training sequence under different codebook layers and different SNR is obtained by beam detection. Moreover, we particularly design an optimal beam pattern synthesis codebook for the hierarchical multi-beam search by using the design method of digital filter, which is applicable in other hierarchical search methods as well. Simulation results show that the proposed codebook is superior to other codebooks and the proposed multi-beam search method has higher channel estimation accuracy, but requires less training overhead, compared with the state-of-the-art alternatives.