Security-Aware Cross-Layer Resource Allocation for Heterogeneous Wireless Networks

Abstract

In this paper, a security-aware energy-efficient resource allocation is modeled as a fractional programming problem for heterogeneous multi-homing networks. The security-aware resource allocation is formulated as a secrecy energy efficiency maximization problem subject to the average packet delay, the average packet dropping probability, and the total available power consumption. In order to guarantee the packet-level quality of service (QoS), first, the average packet delay and the average packet dropping probability requirement for each mobile terminal at the link layer are transformed into a minimum secrecy rate constraint at the physical layer. Then, the non-convex secrecy energy efficiency maximization problem is approximated by a convex problem through epigraph representation. A security-aware energy-efficient resource allocation algorithm is then proposed leveraging dual-decomposition method and bi-section search method. Finally, a heuristic security-aware resource allocation algorithm is proposed to serve as a benchmark. Simulation results demonstrate that the proposed security-aware energy-efficient resource allocation algorithm not only improves the secrecy energy efficiency and throughput, but also guarantees the packet-level QoS.

Machine-to-machine and cell-to-cell traffic handling using relay and carrier aggregation prioritize on LTE-A PRO network

“…The metrics of evaluation are average packet-dropping probability and average user throughput. The delay-sensitive communication algorithm considers the availability of resources before their allocation to minimize packet-dropping probability that is caused by packet-exceeding time delay (Xu et al, 2019). The evaluations are done using Vienna LTE-A Simulator (Vienna University of Technology (Institute of Telecommunications, 2016) for link-level simulation.…”

Social-sine cosine algorithm-based cross layer resource allocation in wireless network

“…The performance increased by the developed method is evaluated by comparing the proposed with existing methods, like waterfilling method [16], Security aware energy efficient allocation [19], distributed energy efficient allocation [24], respectively. Figure 2 shows the comparative of the developed SSCA-based allocation with respect to energy, throughput and the fairness with respect to users.…”

“…Here, the novel resource allocation strategy is developed using the proposed SSCA, which will be newly designed by integrating SSD [22] and SSA [23]. Also, for further refining the resource allocation scheme, the proposed SSCA uses the fitness based on energy [19] and fairness where the hard-fairness, proportional fairness, mixed-bias max-min, and maximum throughput is considered. The block diagram of the proposed SSCA-based resource allocation in the wireless network is shown in Figure 1.…”

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends and Challenges

“…4, the key issues of RA in traditional HetNets (e.g., TDMA) are optimal power allocation (i.e., power control, RA), user association (i.e., the user uses which BS to communicate) and bandwidth allocation. [59] Fairness [49], [63] Mean rate [51] Distributed algorithm [63] Water-filling [51], Match theory [56] Macro-small [53], [58], [63] allocate power [48]- [63] Secrecy rate [54] Centralized algorithm (others) Iteration algorithm [55], [58], [63] Macro-femto [56], [60]- [62] bandwidth [48], [58] EE [52], [55], [57], [58], [60]- [62] Imperfect CSI [59]- [61] Convex optimization [48], [52]- [54], [59]- [62] Macro-pico [50]- [52], [54], [55], [57] User association [49], [51], [53], [55] Throughput, rate [48]- [50],…”