In this letter, we investigate the ergodic capacity of the intelligent reflecting surface (IRS)-assisted communication system with quantization phase errors, which is different from existing works assuming ideal continuous or discrete phases. The ergodic capacity, however, does not admit an exact closed-form expression if not impossible. In order to gain insight into the capacity performance, the impact of phase errors on the capacity degradation is quantified, and the minimum number of the reflectors to achieve a given rate threshold is obtained. Simulation results verify the effectiveness of the IRS-assisted system and the capacity scaling law.

This paper investigates and analyzes the number of reflecting elements for guaranteed energy- and spectral-efficient intelligent reflecting surface (IRS)-assisted communication systems. As opposed to previous works where the energy efficiency (EE)/the spectral efficiency (SE) maximization or the EE-SE tradeoff was considered, our goal is to minimize the number of reflecting elements in the IRS-assisted system. Besides, both the EE and the SE constraints are considered in the number minimization problem, which has not been addressed in existing works. However, both the EE and the SE performance do not admit exact closed-form expressions due to the non-convexity incurred by joint beamforming and phase shift design. In order to make the optimization problem tractable, we resort to their performance bounds for problem reformulation. By decomposing the problem into two sub-problems, we are able to derive closed-form expressions for the minimum number of reflecting elements, and both the coherent phase shift (CPS)-oriented solution and the random phase shift (RPS)-oriented solution are proposed for comparison. In order to shed light on the practical design, the relationship between the derived number of reflecting elements is established for both schemes, and the upper bounds on both the EE and the SE thresholds and the placement of the IRS to achieve only one reflecting element are obtained. Simulation results confirm the validity of our analysis on the minimum number of reflecting elements and effectiveness of both schemes.

Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

The future sixth generation (6G) wireless communication networks will face the challenges of large-scale connections green communication. To meet these requirements, cognitive ambient backscatter communication (C-AmBC) has been proposed as a new spectrum paradigm for the green Internet-of-Things (IoT) with stringent energy and spectrum constraints, in which the backscatter device (BD) can achieve communications by simultaneously sharing both spectrum and radio-frequency (RF) sources. However, due to the broadcasting nature of wireless communication channels, BD is vulnerable to eavesdropping from unlicensed eavesdroppers. To address this, this paper proposes a framework of C-AmBC networks in the presence of an unlicensed eavesdropper. Specifically, we investigate the reliability and security of the proposed framework by invoking the outage probability (OP) and intercept probability (IP) with analytical derivations. In addition, the asymptotic behaviors are conducted for the OP in the high signal-to-noise ratio (SNR) regime and IP in the high main-to-eavesdropper ratio (MER) regime. Extensive analytical and computer simulated performance evaluation results show that: 1) when the considered system is under high SNR, the OP of the legitimate user and BD tends to be a non-zero fixed constant, indicating that the existence of error floors for the diversity orders; 2) the performance trade-off of reliability and security can be optimized by adjusting various parameters of the considered system; 3) with the increase of MER, the security of the legitimate user increases, while that of BD decreases.

In this article, we investigate a communication and computation problem for industrial Internet of Things (IoT) networks, where K relays can help accomplish the computation tasks with the assist of M computational access points. In industrial IoT networks, latency and energy consumption are two important metrics of interest to measure the system performance. To enhance the system performance, a three-hierarchical optimization framework is proposed to reduce the latency and energy consumption, which involves bandwidth allocation, off-loading, and relay selection. Specifically, we first optimize the bandwidth allocation by presenting three schemes for the second-hop wireless relaying. We then optimize the computation off-loading based on the discrete particle swarm optimization algorithm. We further present three relay selection criteria by taking into account the tradeoff between the system performance and implementation complexity. Simulation results are finally demonstrated to show the effectiveness of the proposed three-hierarchical optimization framework.

In this paper, we study a multiuser mobile edge computing (MEC) network, where tasks from users can be partially offloaded to multiple computational access points (CAPs). We consider practical cases where task characteristics and computational capability at the CAPs may be time-varying, thus, creating a dynamic offloading problem. To deal with this problem, we first formulate it as a Markov decision process (MDP), and then introduce the state and action spaces. We further design a novel offloading strategy based on the deep Q network (DQN), where the users can dynamically fine-tune the offloading proportion in order to ensure the system performance measured by the latency and energy consumption. Simulation results are finally presented to verify the advantages of the proposed DQN-based offloading strategy over conventional ones.