Efficiency Resource Allocation for Device-to-Device Underlay Communication Systems: A Reverse Iterative Combinatorial Auction Based Approach

Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.Comment: 26 pages, 6 fgures; IEEE Journals on Selected Areas in Communications, 201

Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks

Device-to-Device (D2D) communication is expected to enable a number of new services and applications in future mobile networks and has attracted significant research interest over the last few years. Remarkably, little attention has been placed on the issue of D2D communication for users belonging to different operators. In this paper, we focus on this aspect for D2D users that belong to different tenants (virtual network operators), assuming virtualized and programmable future 5G wireless networks. Under the assumption of a cross-tenant orchestrator, we show that significant gains can be achieved in terms of network performance by optimizing resource sharing from the different tenants, i.e., slices of the substrate physical network topology. To this end, a sum-rate optimization framework is proposed for optimal sharing of the virtualized resources. Via a wide site of numerical investigations, we prove the efficacy of the proposed solution and the achievable gains compared to legacy approaches.Comment: 10 pages, 7 figure

Spectral and Energy Efficient D2D Communication Underlay 5G Networks: A Mixed Strategy Approach

4G is now deployed all over the world, but requirements are about to change rapidly face to the exponential growth on devices number, local service applications and spectrum scarce. To deal with that, 5G networks integrated Device To Device (D2D) communication as a key technology in its evolving architecture. From 3GPP Rel-12 to Rel-16, D2D succeeded to improve network capacity by enhancing spectrum reuse, data rates and reducing end-to-end latency. However, despite all these advantages, it implies new challenges in 5G system design as interference, spectrum and energy consumption. As a contribution, we propose in this paper a joint spectrum and energy efficient resource allocation algorithm for D2D communications. This approach maximizes the total spectrum efficiency and reduces UEs power consumption. Contrarily to most of previous studies on resource allocation problems considering only centralized and pure strategies approaches, we propose a distributed algorithm based on new mathematical game theory model as an interpretation of mixed strategy non cooperative game. We extend our previous research, by focusing on power consumption issue. Our proposed solution enhances joint SE/EE tradeoff by minimizing interferences and power consumption via a smart RB allocation. This new approach allows users to adopt more accurate strategies and maximize their utilities according to the random network behavior

Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications

Device-to-device (D2D) communication is considered one of the key frameworks to provide suitable solutions for the exponentially increasing data tra c in mobile telecommunications. In this PhD Thesis, we focus on the resource allocation for underlay D2D communications which often results in a non-convex optimization problem that is computationally demanding. We have also reviewed many of the works on D2D underlay communications and identi ed some of the limitations that were not handled previously, which has motivated our works in this Thesis. Our rst works focus on the joint power allocation and channel assignment problem in the D2D underlay communication scenario for a unicast single-input and single-output (SISO) cellular network in either uplink or downlink spectrums. These works also consider several degrees of uncertainty in the channel state information (CSI), and propose suitable measures to guarantee the quality of service (QoS) and reliability under those conditions. Moreover, we also present a few algorithms that can be used to jointly assign uplink and downlink spectrum to D2D pairs. We also provide methods to decentralize those algorithms with convergence guarantees and analyze their computational complexity. We also consider both cases with no interference among D2D pairs and cases with interference among D2D pairs. Additionally, we propose the formulation of an optimization objective function that combines the network rate with a penalty function that penalizes unfair channel allocations where most of the channels are assigned to only a few D2D pairs. The next contributions of this Thesis focus on extending the previous works to cellular networks with multiple-input and multiple-output (MIMO) capabilities and networks with D2D multicast groups. We also present several methods to accommodate various degrees of uncertainty in the CSI and also guarantee di erent measures of QoS and reliability. All our algorithms are evaluated extensively through extensive numerical experiments using the Matlab simulation environment. All of these results show favorable performance, as compared to the existing state-of-the-art alternatives.publishedVersio