Reconfigurable intelligent surface (RIS) has emerged as a promising technology for achieving high spectrum and energy efficiency in future wireless communication networks. In this paper, we investigate an RIS-aided single-cell multi-user mobile edge computing (MEC) system where an RIS is deployed to support the communication between a base station (BS) equipped with MEC servers and multiple single-antenna users. To utilize the scarce frequency resource efficiently, we assume that users communicate with BS based on a non-orthogonal multiple access (NOMA) protocol. Each user has a computation task which can be computed locally or partially/fully offloaded to the BS. We aim to minimize the sum energy consumption of all users by jointly optimizing the passive phase shifters, the size of transmission data, transmission rate, power control, transmission time and the decoding order. Since the resulting problem is non-convex, we use the block coordinate descent method to alternately optimize two separated subproblems. More specifically, we use the dual method to tackle a subproblem with given phase shift and obtain the closed-form solution; and then we utilize penalty method to solve another subproblem for given power control. Moreover, in order to demonstrate the effectiveness of our proposed algorithm, we propose three benchmark schemes: the time-division multiple access (TDMA)-MEC scheme, the full local computing scheme and the full offloading scheme. We use an alternating 1-D search method and the dual method that can solve the TDMA-based transmission problem well. Numerical results demonstrate that the proposed scheme can increase the energy efficiency and achieve significant performance gains over the three benchmark schemes.

Intelligent reflecting surface (IRS) consisting of massive passive elements is a promising technology to save the hardware cost and energy consumption, but with a challenging channel estimation of IRS transmission links. In this letter, a low-complexity channel estimation method is first proposed for IRS-enhanced multi-user massive multiple-input and multiple-output systems, which can attain the separate channel state information (CSI) of the cascaded link. The acquisition of the separate CSI contributes to the flexibility of channel estimations for the multi-user case with user mobility. The proposed channel estimation consists of direction-of-arrival and path gain estimations, which are implemented by using the limited radio frequency chains and pilots, respectively. Besides, the computational complexity is analyzed for the proposed and existing methods for comparison. Numerical results show that the proposed method can achieve accurate channel estimation with a low complexity.

In a device-to-device (D2D) underlaid massive multiple-input multiple-output system, D2D transmitters reuse the uplink spectrum of cellular users (CUs), leading to cochannel interference. To decrease pilot overhead, we assume pilot reuse (PR) among D2D pairs. We first derive the minimum-mean-square-error estimation of all channels and give a lower bound on the ergodic achievable rate of both cellular and D2D links. To mitigate pilot contamination caused by PR, we then propose a pilot scheduling and pilot power control algorithm based on the criterion of minimizing the sum mean-square-error of channel estimation of D2D links. We show that, with an appropriate PR ratio and a well-designed pilot scheduling scheme, each D2D transmitter could transmit its pilot with maximum power. In addition, we also maximize the sum rate of all D2D links while guaranteeing the quality of service of CUs, and develop an iterative algorithm to obtain a suboptimal solution. Simulation results show that the effect of pilot contamination can be greatly decreased by the proposed pilot scheduling algorithm, and the PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of system spectral efficiency.

In this paper, we consider the network utility maximization problem with various user priorities via jointly optimizing user association, load distribution, and power control in a load-coupled heterogeneous network. In order to tackle the nonconvexity of the problem, we first analyze the problem by obtaining the optimal resource allocation strategy in closed form and characterizing the optimal base station load distribution pattern. Both observations are shown essential in simplifying the original problem and making it possible to transform the nonconvex load distribution and power control problem into convex reformulation via exponential variable transformation. An iterative algorithm with low complexity is accordingly presented to obtain a suboptimal solution to the joint optimization problem. Simulation results show that the proposed algorithm achieves better performance than conventional approaches.

This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.