In this paper, we investigate strategies for the allocation of computational resources using deep reinforcement learning in mobile edge computing networks that operate with finite blocklength codes to support low latency communications. The end-to-end (E2E) reliability of the service is addressed, while both the delay violation probability and the decoding error probability are taken into account. By employing a deep reinforcement learning method, namely deep Q-learning, we design an intelligent agent at the edge computing node to develop a real-time adaptive policy for computational resource allocation for offloaded tasks of multiple users in order to improve the average E2E reliability. Via simulations, we show that under different task arrival rates, the realized policy serves to increase the task number that decreases the delay violation rate while guaranteeing an acceptable level of decoding error probability. Moreover, we show that the proposed deep reinforcement learning approach outperforms the random and equal scheduling benchmarks.

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. We show that both the blocklength-limited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as comparison schemes. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV’s one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first time to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Towards this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the UAV-speed-constrained trajectory design problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Building upon the optimal UAV trajectory structure, we further present a low-complexity UAV trajectory design by first transforming the original problem into an equivalent non-convex problem with only the UAV hovering locations and durations as optimization variables, and then updating the trajectory via the successive convex approximation technique. Our analysis shows that the low-complexity design is guaranteed to converge to a suboptimal solution at a significantly lower complexity irrespective of the geographical network size. Numerical results show that the proposed low-complexity design actually achieves the same performance as the proposed optimal solution, and both of them outperform the benchmark algorithms in prior works under different scenarios.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled multiuser wireless power transfer (WPT) network, where a UAV is responsible for providing wireless energy for a set of ground devices (GDs) deployed in an area. We focus on the design of UAV trajectory subject to the maximum flight speed limit, in order to maximize the minimum harvested energy among GDs over a particular charging duration. Different from prior works that considered simplified linear energy harvesting models, this paper for the first time takes into account the realistic nonlinear energy harvesting model for the UAV trajectory design. However, the formulated trajectory design problem is highly non-convex and has infinite number of variables, thus making it be challenging to be solved optimally. To tackle this difficulty, we adopt the following three-step approach to obtain an efficient solution. First, we rigorously characterize that the optimal trajectory follows a new successive-hover-and-fly (SHF) structure, where the UAV hovers at a certain set of points for efficiently transferring energy, and flies among these hovering points with the maximum speed following certain arcs (not necessarily straight lines). Next, based on this SHF structure, we transform the original problem to a new one for finding a set of turning point variables during the maximum-speed flight, at which the UAV changes the flight direction without hovering. Finally, we use the techniques of convex approximation to solve the transformed problem. According to the convexity of the nonlinear energy harvesting model, we iteratively solve a series of convex optimization problems to update the UAV trajectory towards a high-quality solution. Numerical results show the convergence of the proposed approach, and validate its performance gain over conventional designs.

We consider an unmanned aerial vehicle (UAV) assisting data collection from multiple sensor nodes (SNs). We provide a completion time minimization design via jointly deciding the UAV trajectory and the SN assignment scheme. In particular, we first characterize the fundamental features of the joint optimal solution to the formulated problem. On the one hand, the optimal UAV trajectory is proved following a successive-hover-fly (SHF) structure. Namely, in an optimal solution, the UAV successively visits multiple hovering points and performs hovering with designated duration, while the maximum speed is achieved during the whole flying period between each two hovering points. On the other hand, the optimal SN assignment is characterized to follow a segment-based scheme. Based on the two characterizations, we are motivated to implement SHF structure with turning points in trajectory design and reasonably assume each segment in SHF structure having constant SN assignment. Afterwards, we relax the binary constraints for SN assignments and establish a convex approximation for the reformulated problem, which enables an iterative algorithm. A suboptimal joint solution is obtained via iteratively optimizing the completion time. A realization strategy is also provided for the relaxed solution while assuring the completion of data collection tasks. Finally, the proposed solution is validated and evaluated through numerical results. Both a low complexity and an accurate task completion guarantee of our proposed solution are observed in comparison with the benchmarks.

In this paper, relaying performance is studied under the finite-blocklength regime. The overall error probability of relaying is derived. Moreover, we investigate the blocklength-limited capacity (BL-capacity) of relaying. We prove that the BL-capacity of relaying is quasiconcave in the overall error probability. Therefore, the BL-capacity has a global maximum value, which can be achieved by choosing an appropriate error probability. Through numerical investigations, we validate our analytical model and compare the performance of relaying under the finite-blocklength regime versus the Shannon capacity regime.