In this paper, the millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) are exploited for mobile edge computing (MEC) networks to improve the performance of task offloading. Aiming at improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for mmWave-MEC with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. First, according to the max-min fairness criterion, the CE optimization problem is formulated to jointly optimize the ABF at the base station and the local resource allocation of each user in mmWave-MEC with ABF. An efficient algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF at the BS and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed resource allocation schemes can improve the system CE effectively, and the mmWave-MEC with HBF scheme can obtain higher CE than that with ABF scheme. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

Energy efficiency (EE) optimization of joint power allocation (PA) and beamforming (BF) is studied for multiuser distributed multiple-input multiple-output (D-MIMO) systems with maximum ratio combining. Subject to power budget constraints, a non-convex constrained optimization problem is firstly formulated to maximize EE of single-user D-MIMO. We propose a near-optimal joint design scheme with the block coordinate descent method, which has an effective iterative algorithm to find optimal BF for fixed PA, and an iterative algorithm based on the Dinkelbach methods to find optimal PA for fixed BF. We also develop a low-complexity suboptimal joint scheme, which has closed-form BF and PA by maximizing an effective signal-to-noise ratio for fixed PA and a derived EE upper bound for fixed BF, respectively. It has lower complexity than the near-optimal scheme with slight performance loss. With these results, two joint design schemes are developed for multiuser D-MIMO. The first scheme can achieve the best performance, while the second one based on closed-form PA and BF has slight performance loss in comparing to that of the former, but with lower complexity. Simulation results verify the effectiveness of the developed schemes over some existing schemes.