In this paper, we investigate task offloading and computing in an urban mobile edge computing (MEC) system assisted by an aerial intelligent reflecting surface (AIRS). To enhance information freshness while saving energy, we formulate a joint optimization problem to minimize the weighted sum of average age of information (AoI) and total energy consumption by jointly optimizing task offloading decisions, resource allocation, and AIRS configuration including its deployment position, phase shifts, and panel size, subject to offloading quality and computing deadline constraints. To tackle the resulting mixed-integer and nonconvex problem, we develop a hierarchical optimization framework based on the objective priority and variable coupling relations. Under this framework, the problem is solved in two stages using quadratic penalty, numerical analysis, and convex optimization techniques. Specifically, an AoI-aware task offloading policy is first designed to maximize information freshness; subsequently, given the obtained offloading policy, an AIRS configuration and resource allocation scheme is proposed to minimize energy consumption. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in reducing both AoI and energy consumption, while exhibiting superior convergence performance.

Tactile teleoperation allows users to physically interact with the remote environment, and revolutionizes the set of new applications like remote surgical operation. Thus, it is sensitive to the reliability and end-to-end (E2E) latency including the sojourn latency, the transmission latency, and the computation latency. For reducing E2E latency and increasing reliability, the grant-free transmission with$K$-repetition and the mobile edge computing (MEC) are usually utilized. In this paper, we study the upper bounds of the tail-distribution function of the E2E latency in tactile teleoperation. First, we analyze the success probability of transmission, and formulate the statistical model of service process with consideration of the grant-free access, the$K$-repetition and the pilot collision. Then, based on martingales theory, the upper bounds of the tail-distribution of E2E latency are derived with joint consideration of the computation latency of MEC. Finally, the minimum E2E latency bound violation probability is obtained by allocating the latency budgets of uplink latency and MEC computation latency. Simulation results show that our theoretical analysis is accurate and the allocation is feasible.

Ultrareliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) are two types of services with delay Quality-of-Service (QoS) demands. Considering the random and sporadic URLLC packets arrival feature, slicing puncture is believed to be the suitable method to support coexistence communication scenario of eMBB and URLLC terminals. However, slicing puncture in a short time is not trivial, and needs accurate wireless resource scheduling. At the same time, it may damage the QoS of eMBB service. All of these make the QoS guaranteed scheduling problem more challenging. In this article, we investigate the bandwidth, power allocations, slice puncturing problems to find the way satisfying services’ individual QoS demands. For the scheduling of eMBB service, we formulate a joint optimization problem for bandwidth and power allocations with long-term constraints of queues backlog. To solve this problem, we utilize the Lyapunov drift-plus-penalty (DPP) method to establish the relationship between the long-term constraints and the short-term optimization problem, which means that the long-term constraints are gradually satisfied by the proposed strategy at each step. We further divide the short-term optimization problem into two subproblems and adopt a block coordinate descent (BCD) algorithm to reduce the computation complexity. Then, we put forward a one-to-one matching method to solve the integer programming in resource block (RB) allocation and slicing puncture problems. Numerical results demonstrate that the proposed dynamic resource allocation and puncturing strategy (DRAPS) can solve the scheduling problem of eMBB and URLLC services in the presence of multiple randomness of channel-state information (CSI) and URLLC packet arrival.

Intelligent reflecting surfaces (IRS) can improve the physical layer security (PLS) by providing a controllable wireless environment. In this paper, we propose a novel PLS technique with the help of IRS implemented by an intelligent mirror array for the visible light communication (VLC) system. First, for the IRS aided VLC system containing an access point (AP), a legitimate user and an eavesdropper, the IRS channel gain and a lower bound of the achievable secrecy rate are derived. Further, to enhance the IRS channel gain of the legitimate user while restricting the IRS channel gain of the eavesdropper, we formulate an achievable secrecy rate maximization problem for the proposed IRS-aided PLS technique to find the optimal orientations of mirrors. Since the sensitivity of mirrors’ orientations on the IRS channel gain makes the optimization problem hard to solve, we transform the original problem into a reflected spot position optimization problem and solve it by a particle swarm optimization (PSO) algorithm. Our simulation results show that secrecy performance can be significantly improved by adding an IRS in a VLC system.

In next-generation wireless networks, providing secure transmission and delay guarantees are two critical goals. However, either of them requires a concession on the transmission rate. In this article, we consider a visible light network consisting of multiple access points and multiple users. Our first objective is to mathematically evaluate the achievable rate under constraints on delay and security. The second objective is to provide a cell formation with customized statistical delay and security guarantees for each user. First, we propose a user-centric design called secure cell formation, in which artificial noise is considered, and flexible user scheduling is determined. Then, based on the effective capacity theory, we derive the statistical-delay-constrained secrecy rate and formulate the cell formation problem as a stochastic optimization problem (OP). Further, based on the Lyapunov optimization theory, we transform the stochastic OP into a series of evolutionary per-slot drift-plus-penalty OPs. Finally, a modified particle swarm optimization algorithm and an interference graph-based user-centric scheduling algorithm are proposed to solve the OPs. We obtain a dynamic independent set of scheduled users as well as secure cell formation parameters. Simulation results show that the proposed algorithm can achieve a better delay-constrained secrecy rate than the existing cell formation approaches.

Enhanced mobile broadband (eMBB) is a major scenario in the prospective fifth generation (5G) wireless communications. Achieving high user experienced data rate (UEDR) is one of vital goals of eMBB, which needs to consume massive bandwidth. Considering the limited radio frequency (RF) spectrum resource has got heavily congested, we explore the multi-packet reception (MPR) aided visible light communications (VLCs) system to support eMBB services in this paper. MPR can efficiently alleviate collisions for VLC system and enhance UEDR. However, the implementation of MPR in random access system is challenging because of dynamic and unpredictable transmissions of eMBB terminals. For fully utilizing MPR capability and promoting UEDR maximally, we design a two-period frame structure including the access request period (ARP) and the data transmission period (DTP). UEDR emphasizes not only the data rate measured in physical layer but also the network operation behaviour experienced by users which connects closely with quality of service (QoS). In this paper, effective capacity (EC) is employed to depict UEDRs for eMBB terminals for the first time, which provides an efficient link model to evaluate data rates with consideration of statistical delay QoS. Then, the aggregate UEDR is derived and defined as a system metric, which reflects the transmission features of both two periods. We formulate the hybrid access algorithm for eMBB terminals with heterogeneous QoS requirements as an aggregate UEDR maximization problem and find the optimum access probability (for ARP) and optimal resource allocation strategy (for DTP) via the particle swarm optimization-initialization intervention (PSO-II) algorithm.