Energy efficiency (EE) is an important metric of both the communication and sensing systems, which has not been studied in the reconfigurable intelligent surface (RIS) assisted integrated sensing and communication (ISAC) system with partial blocked direct links. In this paper, we consider the EE optimization of the RIS assisted ISAC system with multiple users and targets in a new and common scenario where the direct links between the base station (BS) and targets are blocked. Through analyzing the expression of the EE and signal relation, the EE maximization is modeled as an optimization problem with the constraints of sensing mutual information, transmit power of the BS, and the reflecting coefficients restrict of the passive RIS. The problem can be solved by finding out the optimal beamforming matrix and RIS phase shift. As the problem is a complex non-convex optimization problem and optimization variables are coupled, an effective approximation algorithm based on the alternating optimization is proposed to maximize the EE of the communication part. The original problem is first decomposed into two subproblems. And then, by using the sequential convex approximation and Dinkelbach algorithm, these two subproblems are alternately solved, and the beamforming matrix and the RIS phase shift are alternately optimized accordingly. The simulation results show that the proposed algorithm can converge to the optimized value rapidly and significantly improve the EE performance.

Human activity recognition based on Wi-Fi signals has become one part of integrated sensing and communications, which has promising application prospects. Detecting activities across different domains is an important and challenging problem. To reduce model complexity and improve recognition accuracy, we propose a novel approach to realize activity recognition across domains, named WiSDA. The proposed WiSDA contains two parts: data augmentation and a deep learning model. The recursive plots method is employed as the data augmentation to transform Wi-Fi channel state information into images, which can take advantage of the image recognition ability of the latter deep learning model. The proposed learning model utilizes weighted cosine similarity to align feature distributions among sub-domains activated by a deep network layer across different domains, thereby a domain-independent feature representation is generated. Based on this representation, WiSDA can make the recognition decision independent of domains, then the cross-domain recognition accuracy is increased. The numerical results illustrate that WiSDA achieves higher recognition accuracy and has lower complexity. The cross-domain recognition accuracy ranges from 89% to 93% with offline pre-training. Enhancing the pre-trained WiSDA with limited samples boosts cross-domain recognition accuracy to 97%.

In order to improve communication quality in some heavily blocking scenarios, the double-reconfigurable intelligent surface (RIS) is introduced, then there are more channel coefficients to be estimated. To reduce the pilot overhead, an efficient two-phase channel estimation scheme is proposed for passive double-RIS assisted MIMO systems to estimate the dual-reflection cascaded channels. Specifically, RIS 1 operates in a known fixed mode, and RIS 2 operates in a dynamically adjustable mode with the time slot, which can reduce the number of channel coefficients to be estimated. Then, the dual-reflection cascaded channel property is utilized to further reduce pilot overhead. Simulation results illustrate that the proposed scheme significantly reduces the pilot overhead and achieves superior estimation performance simultaneously.

The performance analysis is important for quality of service (QoS) guarantee, which has not been well studied in the interference‐limited system. The authors study the QoS performance of each user in an interference‐limited system when the power control policy is used to maximise the sum rate of users. Since service processes of users are correlated, the authors first propose a two‐dimensional Markov chain to model the transition among queue states of users. Through solving the Markov chain, the authors derive the closed‐form formulae of the stationary probability of the queue length and other QoS metrics of each user. The analysis results indicate that the power allocation problem should be reconsidered in unsaturated networks since the power allocation policy designed for saturated networks is no longer appropriate. The authors thus develop a new power allocation algorithm which considers queue length and channel quality jointly. Finally, the simulation results verify that the authors’ analytical method provides accurate estimations of QoS metrics and the proposed algorithm enhance the power efficiency and improve the queue performance of each user.

The quality of service (QoS) performance analysis is of great guiding significance for a QoS guarantee of secondary users (SUs). However, the QoS performance of SUs has not been well studied, especially how it is impacted by the traffic property of primary users (PUs). In this study, the authors propose a method to analyse the queue performance of the SU when the active and inactive durations of PUs follow general distributions. To characterise the non‐memoryless property of the channel when the distributions of PUs active/inactive durations are general distributions, they propose a two‐dimensional Markov chain to model the states of the channel. By using this Markov chain, they derive the effective capacity (EC) function of the cognitive radio network. On the basis of the EC function and the effective bandwidth function, the queue performance of the SU, that is, the stationary tail distribution of the queue length, is estimated. The author's result can not only be used to calculate other QoS metrics, such as the buffer overflow probability and throughput, but also provide some guidelines for QoS guarantee of the SU. Finally, they verify their work by comparing the analytical results with simulation results.

In this paper, we propose interference alignment (IA) schemes for downlink multiple-input-multiple-output heterogeneous networks (HetNets) with partial connectivity, which is induced by the path loss and the low transmission power of small cells. Specifically, we consider two partially connected scenarios of HetNets. In the first scenario, we focus on the partial connectivity among small cells, whereas in the second scenario, we further consider the partial connectivity between the macrocell and small cells. For the first scenario, we first propose a two-stage IA scheme by exploiting the heterogeneity and partial connectivity of HetNets. Then, the influence of the number of served macro users on system degrees of freedom (DoFs) is investigated. In particular, we derive the condition under which serving one macro user achieves more DoFs than serving multiple macro users and design an algorithm to find the optimal number of served macro users to maximize the system DoFs. Afterward, we study the second scenario and extend the two-stage IA to this scenario. The simulation results show that the proposed IA schemes can significantly improve the system sum rate. Moreover, by considering the partial connectivity between the macro cell and small cells, the system performance can be further improved.

Admission control is important in achieving QoS guarantees in multi-hop wireless networks. An efficient admission control algorithm requires an accurate estimation of the end-to-end delay distribution of the network. In this paper, we propose a method to estimate the end-to-end delay distribution under the general traffic arrival process and Nakagami-m channel model. We firstly propose a novel two-dimensional Markov Chain to model the node behaviors in a multi-hop multi-rate IEEE 802.11 network that is subject to interference and error prone channel. By combining the basic Probability theory and Network Calculus, we analyze the delay a packet experiences at each hop along a path. The per-hop delay result is used to develop the distribution of the end-to-end delay of a randomly chosen path. We then develop an admission control scheme for the traffic with stochastic QoS guarantees. Finally, through simulation results, we verify the accuracy of our analytical model and the effectiveness of the proposed algorithm.

In this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as$[O(1/V),O(V)]$with$V$as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.

End-to-end delay analysis is an important element of network performance analysis in multi-hop wireless networks. In this paper, we analyze the end-to-end delay of wireless networks with general traffic model and capacity-varying channel. A new concept of residual effective capacity using Effective Bandwidth theory and Effective Capacity theory is presented, which allows us to calculate the cumulative distribution function of queuing delay. We derive a formula to calculate the average end-to-end delay for multi-hop wireless networks and validate our analysis through simulations.