Heterogeneous Traffic Offloading in Space-Air-Ground Integrated Networks

Abstract

While the 5-th generation of communication networks (5G) is taking its first deployment steps, high doubts still concern the fulfilment of the stringent requirements of its slices. To meet these complex requirements, robust access networks should support the current 5G air interface. In this regard, space and air networks, namely satellites and unmanned aerial vehicles (UAVs) are expected to play a key role due to their wide coverage and scalable deployment respectively. Currently, the integration of these platforms in the terrestrial networks is weak though. Therefore, we suggest bridging this gap by designing a heterogeneous traffic offloading approach in the space-air-ground integrated network (SAGIN). Our innovative offloading approach covers the co-existing requirements of two heterogeneous slices of 5G by offloading smartly the traffic to the appropriate segment of SAGIN. Specifically, the ultra-reliable lowlatency communications (URLLC) traffic is offloaded to the UAV link and to the terrestrial link to satisfy its stringent requirements in terms of latency. However, the enhanced mobile broadband (eMBB) traffic is offloaded to the UAV link, to the terrestrial link and to the satellite link because it is less sensitive to delay but needs high data rates. Our offloading approach boosts the network's availability and reduces the latency experienced in SAGIN through an efficient resource allocation and an optimized design of the UAVs trajectory. Our findings highlight the key role that the concrete integration between SAGIN segments plays to achieve a better quality of service (QoS) for different slices with heterogeneous requirements.

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

“…The algorithm adeptly allocates heterogeneous network resources in SAGIN by amalgamating heuristic algorithms and deep reinforcement learning [61] . Moreover, multi-objective optimization issues are employed to consider the QoS requirements for different traffic types simultaneously, especially for enhanced mobile broadband (eMBB) and ultra reliable low latency communications (URLLC) slices [116] .…”

“…In summary, multi-agent learning, particularly through advanced algorithms like LAMAPPO, CL-MADDPG, and MADDPG, offers a pathway to effectively manage the resource allocation challenges in SAGIN. These methods allow for decentralized decision-making, where each agent learns to cooperate with others, leading to improved overall network Propose RL-BA-VNA, a reinforcement learning-based algorithm for virtual network resource allocation that optimizes node embedding and prioritizes high-bandwidth virtual network requests [111] Maximize the energy efficiency of SAG IoT Networks A two-tier SAG IoT HetNets consisting of macrocell base stations and aerial base stations Energy efficiency, total system data rates, total system power consumption Propose a cluster-based HetNets energy-efficient resource allocation (CHERA) mechanism that divides the network into independent base station (BS) clusters for distributed EE optimization [112] Maximum safety SAGIN for HSRs, LEO, GEO,terrestrial base stations Data rate, bit error rate, end-to-end latency Establish a dual C-plane connection, introduce gain and handover factors for safety service prioritization, and implement a Q-learning algorithm considering train and satellite movements for resource allocation [12] Minimize the task completion time and satellite resource usage Vehicles in remote areas, edge computing-enabled LEO, MEO, GEO, ground cloud servers Task completion time, satellite resource usage, system reward performance, accuracy of offloading and caching actions Present a preclassification scheme to reduce the action space and propose a deep imitation learning (DIL)-driven offloading and caching algorithm for real-time decision-making [29] Maximum end-to-end QoE Utilize DRL to optimize the multi-domain VNE algorithm for SAGIN, incorporating network modeling, attribute setting, policy network implementation, and evaluating algorithm efficiency through simulations [59] Improve global data transmission capacity LEO, UAVs, SDN architecture Transmission capacity of SAGIN, network load balancing Predict the transmission capacity of links in SAGIN, formulate the traffic scheduling problem as a modified maximum flow problem, and employ a DRL model to make global optimal traffic scheduling decisions [60] Maximize the sum log spectral efficiency Multibeam GEO Satcom system Sum log spectral efficiency Implement an enhanced DRL algorithm based on TD3 with independent training, prioritized experience replay, scaling factor, and noise rebound to address the bound action problem, and demonstrate its superior performance through simulations compared to baseline schemes [114] Proactive SDN-based resource management considering user priorities, latency, SLA, and budget constraints in SAGIN [61] Minimize energy consumption Aircraft networks, LEO, HAPs, SDN, NFV, and MEC technologies Service request reception rate, end-to-end delay, and average energy consumption Implemented a hierarchical SAGIN-MEC structure and developed a DRL-G algorithm combining heuristics with deep reinforcement learning for resource allocation [116] Minimize latency Satellites, UAVs, terrestrial networks, eMBB and URLLC slices Network availability, latency experienced by eMBB and URLLC traffic, service costs Implement a deep reinforcement learning (DRL) approach using the deep deterministic policy gradient (DDPG) algorithm for efficient resource allocation and UAV traje...…”

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

“…Guo et al [17] investigated service coordination to guarantee QoS in SAGIN service computing and proposed an orchestration approach to achieve a low-cost reduction in service delay. To achieve a better latency/QoS for different 5G slices with heterogeneous requirements, Zhang et al [18] intelligently offloaded traffic to the appropriate segment of the SAGIN. In order to ensure QoS for airborne users, as well as to reduce energy consumption, Chen et al [19] designed an energy-efficient data-saving scheme, with extensive simulations confirming the effectiveness of the proposed scheme in terms of both energy consumption and processing delay in a SAGIN.…”