The deployment of multiple intelligent reflecting surfaces (IRSs) in blockage-prone millimeter wave (mmWave) communication networks have garnered considerable attention lately. Despite the remarkably low circuit power consumption per IRS element, the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs, resulting in lower overall energy efficiency (EE). To tackle this challenge, we propose a flexible and efficient approach that individually controls the status of each IRS element. Specifically, the network EE is maximized by jointly optimizing the associations of base stations (BSs) and user equipments (UEs), transmit beamforming, phase shifts of IRS elements, and the associations of individual IRS elements and UEs. The problem is efficiently addressed in two phases. First, the Gale-Shapley algorithm is applied for BS-UE association, followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beam- forming, phase shifts, and element-UE associations. To reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks, we introduce an efficient algorithm to solve the associations between IRS elements and UEs. Numerical results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

In the rapidly evolving landscape of wireless communications, the integration of intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) has emerged as a transformative paradigm for mobile computing. With the proliferation of data-intensive applications, improving latency purely from a communication perspective no longer suffices to meet the demand on ultra low latency. Unlike prior efforts focusing on IRS-enhanced communication, this letter harnesses the IRSs’ beam manipulation capabilities to improve computation efficiency, where the task of each user equipment (UE) is divided and executed both locally and at multiple edge servers concurrently. Through the joint optimization of IRS phase shifts, UE-IRS associations, offloading ratio, and task distribution ratio, the IRS-enhanced MEC system aims at minimizing maximum delay. Since the optimization problem is non-convex and NP-hard, the problem is decomposed into two subproblems and solved using an efficient block coordinate descent-based algorithm employing the penalty method, successive convex approximation, and reformulation linearization technique. The proposed algorithm converges quickly, with reduced delay up to 49.14% compared to the existing IRS-aided communication scheme.

Near-zero-power backscatter communication (BackCom) based on ambient stray electromagnetic waves has become a promising technique for building passive Internet of Things (IoT). Nevertheless, BackCom is confined by both spectrum and energy availability, making it challenging to guarantee signal quality and transmission stability in unpredictable wireless environments, especially in multi-hop wireless networks. In this article, we propose a novel hybrid reflect-decode-forward (HRDF) relaying with metasurface-enhanced backscatter communication (Meta-BackCom) devices to achieve efficient forwarding in a scalable passive IoT network. Several HRDF relaying cases are then explored to enhance the robustness and capacity of Meta-BackCom networks. Furthermore, an online robust co-relay (RCoR) scheme is designed for multi-hop networks with HRDF relays, which is an online routing policy with adaptive forwarding mode and relay link decision, accounting for dynamic network environment. Numerical results reveal that the proposed RCoR with HRDF relaying strategy can significantly improve the spectrum efficiency, energy efficiency, and outage performance for multi-hop Meta-BackCom networks.

In the ever-evolving realm of wireless communications, the integration of intelligent reflecting surface (IRS) and mobile edge computing for task offloading has ignited extensive curiosity. However, previous research primarily concentrated on task offloading assuming that a user equipment (UE) possesses the system channel information and the computing resources of base stations (BSs). Acknowledging that UEs typically lack access to such system information, and in pursuit of equilibrium in computing loads between UEs and BSs, we present a bilateral online task allocation approach grounded in partial offloading to minimize task completion latency. Specifically, addressing uncertainties in channel information and available computing resources at BSs, we employ the online ridge regression method on the UE side to continuously adjust the task allocation proportion for offloading to BSs. On the IRS side, we formulate the BS selection as a multi-armed bandit problem, proposing an online learning algorithm based on Thompson sampling to determine the set of BSs for edge computing while managing the task allocation among the selected BSs. Simulation results demonstrate the superior performance of our algorithm.

With the merit of self-sustainability, ambient backscatter aided wireless networks (AmBWNs) have attracted considerable attention for the potential application in Internet of Everything. The ambient backscatter transmission significantly reduces the power consumption by reflecting or absorbing ambient RF signals to transmit at the cost of fragile performance guarantee. The dual-mode node which can transmit in active mode (ATM) or backscatter mode (BTM) has been proposed to improve both energy efficiency and performance reliability. In the AmBWN composed of dual-mode nodes, information flows and energy flows coexist and are transferable. Existing maximum flow algorithms designed for static networks absence of frequent node and link state transitions are not suitable for AmBWNs. Therefore, we investigate the statistical guarantee of the maximum flow in evolving AmBWNs with energy constraint, flow conservation and Markov inequality constraint. The simulation result shows that the proposed statistical maximum flow algorithm can improve the energy efficiency by 2.5 times under poor channel condition.

Mobile edge computing offers a new paradigm to provide more convenient computing services for mobile devices. However, the mobility of devices and the limited coverage of edge servers bring considerable challenges to efficient computation offloading. Moreover, tasks with temporal dependency further complicate the offloading problem in the mobile edge network. In this paper, we take into account the mobility of devices and the fine-grained tasks generated by the mobile device to make full use of the computing resources of devices and edge servers. Considering the temporal dependency among tasks, the offloading problem is formulated as a mixed integer programming which achieves the tradeoff between time latency and energy consumption. Simulation results demonstrate that our proposed algorithm can achieve a significant improvement in terms of energy efficiency and latency compared with other bench mark algorithms.