Integrated sensing and communication (ISAC) is potentially viewed as a key driver in the ubiquitous service of future wireless systems. However, numerous challenges should be solved before the alluring benefits are enjoyed. Although enhancement of sensing capabilities inevitably leads to a reduction in the communication performance, the loss can be compensated through the emerging reconfigurable intelligent surface (RIS). Specifically, active RIS is more favored because the “multiplicative fading” effect existed in passive RIS is overcome, and the manipulation effectiveness of wireless environments can be further enhanced. To this end, this paper investigates the simultaneous optimization of the transmit beamforming at base station (BS) and the precoding at RIS. To fully explore the potential of the active RIS-enabled ISAC system, two antenna deployment strategies, i.e., separated deployment and shared deployment, are designed to maximize the communication users' sum-rate under the considered constraints including transmission power, probing power and reflection manipulation, etc. Furthermore, an effective algorithm combining fractional programming and semidefinite relaxation is employed to derive a sub-optimal solution through an alternating optimization framework due to the non-convex characteristic of the initial problem. Finally, the simulation results demonstrate that more superior transmit beampatterns can be achieved in the shared deployment in comparison with the separated deployment, and that the sum-rate's upper bound is significantly enhanced in the active RIS when comparing to traditional passive RIS.

The integrated access and backhaul (IAB) architecture is a candidate in the beyond fifth-generation (B5G) era to improve the network capacity and coverage extension. However, real-time changes in the transmission demands in this system bring a few technical challenges in terms of resource management. Aimed at this, the long-term throughput maximized subchannel assignment is investigated in this article. First, the cumulative achievable rate maximized subchannel assignment considered in the system is expressed as a nonlinear and nonconvex (NLNC) optimization problem under the constraints of dynamically changed transmission demands, real-time achievable rate, and available resources. Subsequently, the original problem is equivalently reformulated as a long-term throughput maximization problem within a Markov decision process (MDP) framework. A deep reinforcement learning (DRL)-based subchannel assignment framework using Transformer, named DSAF-Former, is designed to effectively capture long-range dependencies and contextual information of transmission demands. Additionally, the subchannel assignment strategy is dynamically manipulated. Finally, we conducted simulation experiments to assess the performance improvements of the proposed DSAF-Former in various scenarios with other baseline algorithms (deep Q-network, Q-Learning, etc.). Specifically, the average throughput and spectral efficiency (SE) is increased by 129.02% and 104%, respectively, when the Adam optimizer is selected in the proposed DSAF-Former.

Millimeter-wave integrated access and backhaul network (mmWave IABN) is a cost-effective paradigm to accommodate the ever-increasing demands of IoT devices, but its sensitivity to the obstructions is an obstacle in the practical commercial applications. Reconfigurable intelligent surface (RIS) is an innovation enabler to proactively manipulate the ambient environment in a programmable manner for the overall performance enhancement. In this paper, the resource allocation of the RIS-assisted mmWave IABN is conceived and designed to maximize the end-to-end throughput. Firstly, the end-to-end throughput maximization oriented resource allocation problem including transmission power controlling, phase-shift manipulation, and bandwidth allocation is formulated as a non-linear and non-convex programming problem, which is intractable to search an optimal solution in polynomial time. Secondly, the formulated original problem is equivalently decomposed into two subproblems to obtain a sub-optimal solution, i.e., the joint optimization subproblem of transmission power controlling at the users and phase-shift manipulation at the RIS, and the bandwidth allocation subproblem between the access part and the backhaul part. Thirdly, a decomposition iteration based resource allocation mechanism (DIRAM) is proposed, and the DIRAM is composed of two phases, which is the alternative iteration based optimization scheme of power controlling and phase-shift manipulation (AIOS-PCPM) to obtain the optimal solution of the first subproblem, and the piecewise statistical based bandwidth allocation (PSBA) scheme to obtain the optimal solution of the second subproblem. Finally, the properties of the proposed DIRAM are evaluated through abundant of simulation comparisons with other baselines, where the superiorities of the proposed DIRAM are verified in terms of spectral efficiency and end-to-end throughput.

Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this article, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard , using only a pair of speaker and microphone. AudioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, and we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.

One of the key techniques for future wireless network is full-duplex-enabled millimeter wave integrated access and backhaul network underlaying device-to-device communication, which is a 3GPP-inspired comprehensive paradigm for higher spectral efficiency and lower latency. However, the multi-user interference (MUI) and residual self-interference (RSI) become the major bottleneck before the commercial application of the system. To this end, we investigate the sub-channel allocation problem for this networking paradigm. To maximize the overall achievable rate under the considerations of MUI and RSI, the sub-channel allocation problem is firstly formulated as an integer nonlinear programming problem, which is intractable to search an optimal solution in polynomial time. Secondly, a coalition formation based sub-channel allocation (CFSA) algorithm is proposed, where the final partition of the sub-channel coalition is iteratively formed by the concurrent link players according to the two defined switching criterions. Thirdly, the properties of the proposed CFSA algorithm are analyzed from the perspectives of Nash stability and uniform convergence. Fourthly, the proposed CFSA algorithm is compared with other reference algorithms through abundant simulations, and superiorities including effectiveness, convergence and sub-optimality of the proposed CFSA algorithm are demonstrated through the kernel indicators.

Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable operation. However, most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data. To address this challenge, we propose a parallel time-series prediction model called LDformer. First, we combine Informer with long short-term memory (LSTM) to obtain deep representation abilities in the time series. Then, we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention mechanism. Finally, we propose a probabilistic sparse (ProbSparse) self-attention mechanism combined with UniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the sequence. Experimental results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.