Mobile edge computing (MEC) has emerged as a promising solution to alleviate mobile devices' (MUs') computational burden by offloading part or all of their computational tasks to a nearby edge server. To promote the wide deployment of MEC, charging the MUs and rewarding the edge server is a good mechanism to motivate the edge server to offer computing service. Current related literature usually assume a linear pricing strategy, in which the unit price in time is a linear function with the served computing capability. In this work, however, we explore the nonlinear pricing strategy for the first time, as the cost of a CPU presents super-linear feature with the computing capability. A MEC system with multiple MUs and a single edge server is considered and a two-level Stackelberg game with the leader being the edge server and the followers being MUs is formulated, whereby edge server's revenue is maximized in the upper level via optimizing the nonlinear pricing function while the defined cost function of individual MUs is minimized by deciding the amount of data for offloading and computing capability to purchase from the edge server in the lower level. Through analysis, steps of transformations, and relaxation, closed-form optimal solution for lower-level problem is derived, and the solution for upper-level problem is presented although it is non-convex. Numerical results verify the superiority of our proposed pricing strategy over traditional linear pricing strategy.

In mobile edge computing (MEC) supporting multiple mobile users (MUs), it is essential to optimize the offloading policy and communication and computation resource allocation. A main challenge is that the computation burden of a computation task may be random and even with uncertain probabilistic distribution. To address this challenge, we investigate a multiple-MU MEC system with random computation burden. For the random computation burden of an MU, only the mean and variance are known, but its distribution is unknown. Robustness is provided such that computation outage probabilities (due to uncertain distribution of computation burden) are bounded by a predefined threshold. We minimize the weighted sum of the MUs’ energy consumption. The formulated optimization problem is non-deterministic and non-convex, and thus, is hard to solve. To deal with the challenge, we transform the formulated problem into a deterministic and convex problem by applying the Chebyshev-Cantelli inequality and some mathematical manipulations. We further decompose the convex problem to a lower-level and an upper-level problem. Low-complexity algorithms are developed for the lower-level and upper-level problems. The overall complexity of our proposed method is linear with the number of MUs.

This paper investigates the joint task offloading and communication/computation resource allocation in a multiuser mobile edge computing (MEC) system. In particular, we consider the multi-channel scenario, in which there are multiple channels each at a different frequency band, and the spectrum of each channel is shared by one or more mobile users (MUs) for offloading continuously. Our objective is to minimize a weighted sum of the energy consumption at MUs, by jointly optimizing the offloading time duration, the offloaded task data bits, the bandwidth allocations over different MUs at these channels, and the computing frequencies at both MUs and edge servers. Because the formulated problem has coupled optimization variables, it is non-convex and difficult to get the optimal solution. To address the challenge, we propose to decompose the formulated problem into two levels. For the lower-level problem with given offloading time duration, we employ the block coordinate descent framework to optimize two groups of optimization variables iteratively, for each of which the optimal solution is obtained in well structures with low complexity. For the upper-level problem for optimizing the offloading time duration, we find the optimal solution by performing variable manipulations and transforming the problem to a standard monotonic optimization problem. Numerical results show that our proposed method is effective in achieving the global optimality of the energy minimization problem and its advantages over other benchmark schemes.

High-throughput satellite communication based on large-scale multi-beam antennas is a promising technology for sixth-generation (6G) mobile networks. Conventional online calibration methods for large-scale antennas after satellite launch are restricted by communication signals. This paper proposes an online calibration method of amplitude and phase errors without disturbing the normal communication traffic. Two important aspects of calibration are discussed, i.e., closed-form expressions of calibration accuracy and hardware complexity. Simulation results show that theoretical approximation results are close to simulation results when the number of antennas is less than half of the code length. The relative amplitude and phase performance deteriorate by 6.12 dB and 40.96 deg, respectively, when the power is lower than the reference 20 dB. Finally, a calibration experimental system with eight beams is built for verification.

In this paper, the problem of joint radio and computation resource management over multi-channel is investigated for multi-user partial offloading mobile edge computing (MEC) system. The target is to minimize the weighted sum of energy consumption by jointly optimizing transmission time, local and edge computation capacity allocation, bandwidth allocation and data partition. An optimization problem is formulated, which is nonconvex and can not be solved directly. Then, we transform the origin optimization problem into an equivalent convex optimization problem. For general case of multi-user multi-channel, we decouple the convex optimization problem into subproblems and an optimal resource management strategy is obtained by adopting block coordinate descent (BCD) method. To gain further insight, we investigate the optimal resource management strategy for two special cases. First, consider the case of multi-user shares single channel. Since the single-channel optimization problem is reduced from the multi-channel optimization problem, the solution approach of general case can be applied to this case, and the solving algorithm for this case has low computation complexity, which is a combination of analytical and bisection-search methods. Then, for the case of single-user occupies all channel, the optimization problem is simplified and an optimal solving algorithm with closed-form solutions is proposed.

This paper focuses on the problem of utilizing millimeter wave (mmWave) and Terahertz (THz) massive hybrid arrays to serve multiple users simultaneously. The mmWave and THz massive arrays are characterized by wide bandwidth and high gain, leading to extensive application prospects. Moreover, a hybrid structure array can combine multiple antenna signals through the phase shifter network. Compared with the full digital array, it is a cost-effective technique that can be functional with fewer radio frequency (RF) chains. However, owing to the adoption of a discrete Fourier transform (DFT) structure, most traditional massive hybrid arrays, which allocate one chain to each user, are restricted to scenarios where the number of RF chains is more than that of users. Otherwise, even users with ideal channel conditions and short distances are inherently difficult to assign an independent chain. Thus, it will limit the scale of users that the base station (BS) can support. Inspired by the above analysis, this paper develops a method to provide service for more users with limited RF chains. Firstly, an analog matrix designing method based on the minimax criterion, which enables arrays access to multiple users, is proposed to guarantee each user a good array gain. Secondly, we establish a receiver designing scheme by the GAMP algorithm to receive signals from multiple users at the same time. Additionally, good bit error rate (BER) performance can be obtained under the condition that the observation matrix is not of full rank. Finally, numerical simulations demonstrate the effectiveness of our proposed method.