Recently cellular networks have been densely and heterogeneously deployed indoors and outdoors to expand the network capacity, and thus the in-building propagation loss and the transmit power diversity of access points will exacerbate link heterogeneity and result in partial unidirectional strong interference. To make full use of the strong interference feature, we propose the successive interference cancellation and alignment (SICA) scheme in the K-user interference channel with partial unidirectional strong interference. SICA is designed to transmit two kinds of data streams simultaneously, the alignment streams and superposition streams. The alignment streams will follow the interference alignment criterion to maintain the optimal degrees of freedom (DoF) performance; the superposition streams are handled via successive interference cancellation at all the strongly interfered receivers to improve the overall achievable rate. The joint transceiver designs for SICA is modeled as a weighted sum rate (WSR) maximization problem, and then can be alternately solved for a local optimum according to the optimality equivalence between WSR and its corresponding weighted mean square error (WMMSE) problem. Simulation results have confirmed the sum rate improvement and DoF optimality of the proposed SICA scheme.

In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output (MIMO) interfering broadcast channels (IBC). The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has N receive antennas and the jth base station transmits dj data streams, where j=1Idj=N+1 and N≥2, the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell i scales as ρα where ρ is the normalized transmit power and α0,1, then cell i can achieve αdi degrees of freedom. Simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA.

As one of the most important metrics to sustainably provide communication services in green Industrial Internet of Things (IIoT), the problem of improving energy efficiency has constantly attracted extraordinary concerns from industry so far as to academia. In this paper, we intend to investigate the energy efficiency issue for an Energy Harvesting (EH)-enabled Machine-to-Machine (EH-M2M) communication underlaying Unmanned Aerial Vehicles (UAVs) networks from the perspective of resource management. Specifically, we are aiming at maximizing the average energy efficiency of EH-M2M communications by conjointly considering the EH time slot assignment, transmit power control and bandwidth allocation under the limitations of Quality of Service (QoS) and the available energy status of the EH-M2M devices. However, as the optimization problem is non-convex and NP-hard which is hard to tackle directly, we first transform the primitive objective function into a convex form equivalently by non-linear fractional programming and variable relaxation approach. After that, an iterative algorithm on the basis of Dinkelbach and Lagrangian theory is designed to optimize the resource management strategy. Finally, extensive simulation results demonstrate that the proposed scheme can establish more energy efficient communications compared to the benchmark schemes in different network settings.

The incorporation of reconfigurable intelligent surface (RIS) into cell-free (CF) networks is a promising paradigm to improve the network coverage and energy efficiency (EE). This letter addresses the fairness-based EE problem in spatially correlated RIS-aided CF networks. A joint design of beamformers at access points (APs) and phase shifts at the RIS is conducted in order to maximize the minimum EE of all users. We first derive the closed-form approximate expression of user ergodic rate based on statistical channel state information (CSI). Then, with this expression, we formulate the problem as a max-min fractional programming and propose an iterative algorithm to solve it under the framework of alternating optimization (AO). Simulation results demonstrate the effectiveness of the proposed algorithm by comparing with a few benchmark schemes.

This letter investigates the resource allocation for Digital Twin-empowered Wireless Body Area Networks (DT-WBANs). We consider a scenario, in which multiple heterogeneous invasive and/or noninvasive bio-sensors are deployed to constantly monitor the patient's physiological signals and wirelessly communicating with a central hub to reliably transmit the sensory data in real-time to paramedic. Specifically, in order to maximize the energy efficiency of the bio-sensors meanwhile guaranteeing the reliability of the emergency-critical physiological data transmission and modeling the non-determinacy of the stochastic environment, we transform the problem into a Markov Decision Process (MDP) by jointly considering the transmission mode, relay selection, transmission power and time slot allocation. Afterwards, a Random Graph-inspired Deep Deterministic Policy Gradient (RG-DDPG) algorithm is proposed to solve the problem and reducing computational complexity. Simulation results verified the feasibility of the proposed scheme.

With the advancement of deep learning, WiFi-based action recognition methods using Channel State Information (CSI) rely generally on domain-specific training, and results in performance degradation in unseen domains, which remains a significant challenge. To address this cross-domain recognition, some complexity models are proposed. However, these works mostly rely on multiple WiFi transceivers which is not common in our daily life. To improve the recognition efficiency and reduce the transceiver requirement, we propose a novel framework for the single transceiver scenario which integrates a recursive plots-based CSI sample enhancement strategy with a multi-source domain adaptation approach. The CSI sample is firstly enhanced by using recursive plots. Then, a lightweight convolutional neural network with integrated spatial attention is used to extract initial domain-invariant features. Subsequently, finegrained feature is extracted through using dedicated sub-networks. This process aligns the target domain with each source domain and regularizes the target domain outputs across multiple classifiers, thereby enhancing the network’s feature extraction. The proposed model is evaluated on the publicly available Widar3.0 dataset. The results indicate that the proposed method can achieve accuracy rates of 92.6% and 90.2% for cross-location and cross-orientation recognition in single-link scenarios, respectively, and effectively reduce the complexity.