Time allocation optimization and trajectory design in UAV-assisted energy and spectrum harvesting network

The scarcity of energy resources and spectrum resources has become an urgent problem with the exponential increase of communication devices. Meanwhile, unmanned aerial vehicle (UAV) is widely used to help communication network recently due to its maneuverability and flexibility. In this paper, we consider a UAV-assisted energy and spectrum harvesting (ESH) network to better solve the spectrum and energy scarcity problem, where nearby secondary users (SUs) harvest energy from the base station (BS) and perform data transmission to the BS, while remote SUs harvest energy from both BS and UAV but only transmit data to UAV to reduce the influence of near-far problem. We propose an unaligned time allocation scheme (UTAS) in which the uplink phase and downlink phase of nearby SUs and remote SUs are unaligned to achieve more flexible time schedule, including schemes (a) and (b) in remote SUs due to the half-duplex of energy harvesting circuit. In addition, maximum throughput optimization problems are formulated for nearby SUs and remote SUs respectively to find the optimal time allocation. The optimization problem can be divided into three cases according to the relationship between practical data volume and theoretical throughput to avoid the waste of time resource. The expressions of optimal energy harvesting time and data transmission time of each node are derived. Lastly, a successive convex approximation based iterative algorithm (SCAIA) is designed to get the optimal UAV trajectory in broadcast mode. Simulation results show that the proposed UTAS can achieve better performance than traditional time allocation schemes

Global repair bandwidth cost optimization of generalized regenerating codes in clustered distributed storage systems

In clustered distributed storage systems (CDSSs), one of the main design goals is minimizing the transmission cost during the failed storage nodes repairing. Generalized regenerating codes (GRCs) are proposed to balance the intra-cluster repair bandwidth and the inter-cluster repair bandwidth for guaranteeing data availability. The trade-off performance of GRCs illustrates that, it can reduce storage overhead and inter-cluster repair bandwidths simultaneously. However, in practical big data storage scenarios, GRCs cannot give an effective solution to handle the heterogeneity of bandwidth costs among different clusters for node failures recovery. This paper proposes an asymmetric bandwidth allocation strategy (ABAS) of GRCs for the inter-cluster repair in heterogeneous CDSSs. Furthermore, an upper bound of the achievable capacity of ABAS is derived based on the information flow graph (IFG), and the constraints of storage capacity and intra-cluster repair bandwidth are also elaborated. Then, a metric termed global repair bandwidth cost (GRBC), which can be minimized regarding of the inter-cluster repair bandwidths by solving a linear programming problem, is defined. The numerical results demonstrate that, maintaining the same data availability and storage overhead, the proposed ABAS of GRCs can effectively reduce the GRBC compared to the traditional symmetric bandwidth allocation schemes

Rateless coding transmission over multi-state dying erasure channel for SATCOM

Abstract Satellite communication (SATCOM) systems have attracted great attention from academic and industrial communities in recent years, and huge amount of data delivery over satellite downlinks is considered as a promising service in emerging 5G networks, such as multimedia broadcasting. Nevertheless, due to intermittent connections from LEO or MEO satellite to earth station, and high dynamic channel conditions over downlinks, satellites may not be able to transmit the large data files to the ground station on time. In this paper, we propose a new rateless coding transmission for multi-state dying erasure channels (MDEC) with random channel life span and time-varying packet error rates, to improve the transmitting capability over SATCOM downlinks. Firstly, a heuristic approach for suboptimal degree distributions based on AND-OR tree technique is presented to achieve higher intermediate performance and lower symbol error rate of our proposed rateless codes. Furthermore, the appropriate code length of the connective window is derived and analyzed for enhanced average throughput on MDEC that is also optimized by maximum problem solving. Simulations have been conducted to evaluate the effectiveness of our rateless coding transmission for large file delivery on dynamic channel conditions. The results demonstrate that our proposed transmission scheme outperforms existing conventional rateless codes with significantly better intermediate performance and throughput performance over unreliable SATCOM downlinks, under time-varying packet error rates and unpredictable occurrences of exhausted energy or cosmic ray attacks

Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks

Cache-enabled unmanned aerial vehicles (UAVs) have been envisioned as a promising technology for many applications in future urban wireless communication. However, to utilize UAVs properly is challenging due to limited endurance and storage capacity as well as the continuous roam of the mobile users. To meet the diversity of urban communication services, it is essential to exploit UAVs’ potential of mobility and storage resource. Toward this end, we consider an urban cache-enabled communication network where the UAVs serve mobile users with energy and cache capacity constraints. We formulate an optimization problem to maximize the sum achievable throughput in this system. To solve this problem, we propose a deep reinforcement learning-based joint content placement and trajectory design algorithm (DRL-JCT), whose progress can be divided into two stages: offline content placement stage and online user tracking stage. First, we present a link-based scheme to maximize the cache hit rate of all users’ file requirements under cache capacity constraint. The NP-hard problem is solved by approximation and convex optimization. Then, we leverage the Double Deep Q-Network (DDQN) to track mobile users online with their instantaneous two-dimensional coordinate under energy constraint. Numerical results show that our algorithm converges well after a small number of iterations. Compared with several benchmark schemes, our algorithm adapts to the dynamic conditions and provides significant performance in terms of sum achievable throughput

Energy-Aware Coded Transmission Strategy for Hierarchical Cooperative Caching Networks

With the rapid development of millions more edge base stations (EBSs) and billions of user devices in the future networks, the desire for low-energy system design and transmission strategy will be even more compelling. In this letter, we investigate the energy consumption issue of hierarchical cooperative caching networks (HCCNs) in which both EBSs and users are capable of storing content data in their local caches. A three-stage coded transmission strategy in HCCNs is proposed, and the closed expression of energy consumption of each stage is derived. In addition, we formulate a total energy consumption minimization problem with the constraint of EBSs’ cache sizes and provide an algorithm to obtain the optimal cache placement matrix. We demonstrate by numerical results that our optimized three-stage coded transmission strategy can achieve lower energy consumption than various multicast transmission strategies under the different users' cache sizes

Repair bandwidth cost of generalized regenerating codes for clustered distributed storage

When repairing storage nodes in a clustered distributed storage system (CDSS), it is crucial to distinguish the intra-cluster and inter-cluster bandwidth costs differing sharply. From this perspective, Generalized Regenerating Codes (GRCs) involving two-layer repair processes was proposed previous and proved as reaching a better trade-off between storage overhead and inter-cluster repair bandwidth. However, due to the lack of explicit expression about the GRCs' parameters for any point on the trade-off curve, it is difficult to determine the optimal GRCs' parameter configuration for reducing the total repair bandwidth cost in a practical CDSS. To address this issue, we devise a novel transmission cost model of CDSS, and initially propose two essential concepts - Cost Coefficient (CC) and Global Repair Bandwidth Cost (GRBC) to denote the unit and total transmission costs of repair bandwidths, respectively. Moreover, we parameterize the two extreme points on the optimal storage overhead versus repair bandwidth trade-off curve, termed Minimum Storage Generalized Regenerating Codes (MS-GRCs) and Minimum Inter-cluster Bandwidth Generalized Regenerating Codes (MB-GRCs), and theoretically analyze the relationships between their GRBCs and the number of local helper nodes £ (the helper nodes in the cluster with failure node). Our mathematical results provide a guidance for employing GRCs to achieve the more efficient node repairing method in CDSS

Adaptive data storage system for mobile satellite-terrestrial IoT

We address the problems of the reliable data storage and the low power data repair for the mobile satellite-terrestrial Internet of Things (IoT) scenario. Firstly, an adaptive satellite-terrestrial IoT distributed storage system consisting of one satellite and many sensor nodes is established for massive contents storing and sharing. Considering the nodes leaving from the satellite coverage subject to the different random processes, to maintain the stability of the system, a novel redundancy fault-tolerant scheme based on Adaptive Minimum Storage Regenerating (AMSR) code is proposed where threshold-based repair process is initiated after a threshold number of nodes have been lost due to the departure of nodes and power exhausted. Furthermore, we derive the analytical expressions of the average communication power costs of node downloading, repairing and monitoring as the functions of repair threshold. We also give the optimal repair threshold for minimizing the overall average communication power cost. Simulation results verify that our AMSR storage system has lower overall average communication power cost than existing MSR and MDS storage systems in the satellite-terrestrial IoT networks