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Recently proposed splitting receivers, utilizing both coherently and non-coherently processed signals for detection, have demonstrated remarkable performance gain compared to conventional receivers in the single-antenna scenario. In this paper, focusing on a single-input multiple-output (SIMO) setup, we propose a multi-antenna splitting receiver, where the received signal at each antenna is split into an envelope detection (ED) branch and a coherent detection (CD) branch, and the processed signals from both branches of all antennas are then jointly utilized for recovering the transmitted information. We derive a closed-form approximation of the achievable mutual information (MI), in terms of the key receiver design parameters including the power splitting ratio at each antenna and the signal combining coefficients from all the ED and CD branches. We further optimize these receiver design parameters and demonstrate important design insights for the proposed multi-antenna ED-CD splitting receiver: 1) the optimal splitting ratio is identical at each antenna, and 2) the optimal combining coefficients for the ED and CD branches are the same, and each coefficient is proportional to the corresponding antenna’s channel power gain. Our numerical results also demonstrate the MI performance improvement of the proposed receiver over conventional non-splitting receivers.

This article presents a hybrid approach to enhance the path planning for unmanned ground vehicles (UGVs) by combining stochastic dynamic programming (SDP) based hybrid model predictive control (HMPC) with Dijkstra-based pseudo priority queues (PPQ). The proposed approach employs a platform-specific model that takes into account skid-slip effects, a global cost-to-go (CTG) function, and dynamic obstacles. To achieve efficient path planning on large maps, the proposed approach employs a Dijkstra algorithm and PPQ to generate the CTG function. Moreover, the HMPC process incorporates the latest CTG information, the vehicle model, and the perceived environment, encompassing both static and dynamic obstacles. Extensive simulations have been conducted to comprehensively evaluate the performance of the proposed technique. Through a comparative analysis against existing path planners, the results demonstrate the effectiveness and superiority of the hybrid approach.

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

We investigate the stability conditions for remote state estimation of multiple linear time-invariant (LTI) systems over multiple wireless time-varying communication channels. We answer the following open problem: what is the fundamental requirement on the multisensor–multichannel system to guarantee the existence of a sensor scheduling policy that can stabilize the remote estimation system? In this note, we propose a novel policy construction and analytical framework and derive the necessary-and-sufficient stability condition in terms of the LTI system parameters and the channel statistics.

Remote state estimation where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multiple-access and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants’ parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.