Energy-Efficient Power Allocation for Millimeter Wave Beamspace MIMO-NOMA Systems

Abstract

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.

A Q-Learning-Based Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems Considering QoS

“…In this section we give the simulation set up in detail and evaluate the performance of the proposed Q-learning based power allocation method (QPA). We use the NOMA system with random power allocation algorithm (NOMA random), orthogonal frequency division multiple acces (OFDMA) and the SRMax method (SRPA) proposed in [35] as benchmarks. Different from NOMA system in which all users share bandwidth B, in OFDMA, each user occupies a bandwidth B/N , where N is the number of users.…”

“…So far, RL has been applied in many fields such as power allocation and user association [27], [28]. Researchers have proposed many ML-based approaches to solve various problem of NOMA system [29]- [31], [33]. In [24], the authors propose a long short-term memory (LSTM) network which can automatically detect the channel characteristics.…”

“…The works in [31] train a deep neural network (DNN) which is used as a predictor to predict the best power allocated to users' signal. The DL based long-term power allocation scheme proposed in [33] can efficiently derive a more accurate decoding order than the conventional solution.…”

Energy Efficiency for Data Offloading in D2D Cooperative Caching Networks

“…Specifically, I 1 = ∑ i∈Φ 1 \cp 0 P t g i r −α i , where g i indicates the interference channel gain of the i-th cache-enabled user and the requesters, whose distance indicates r i . The Laplace Transform of I 1 is as shown in (5). t r α γ 1 into (5), we can obtain (6).…”

“…In Device-to-Device (D2D) communications, mobile devices also play a role in content delivery and enable direct communication links between users. The D2D communications can increase cellular network throughput, reduce energy consumption, and improve spectrum utilization, which can also promote the research of the 5th genera-tion (5G) mobile communication technology [4,5]. Cacheenabled D2D communications have been shown to achieve significant offloading gains in networks, and there are higher chances to retrieve the desired data pieces right from the content-related users [6].…”