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.
In order to efficiently leverage the radical merits that D2D offers, the interference patterns that are being developed due to the resource reuse need to be limited. To this direction, several works within the literature have been elaborating on devising efficient resource allocation and mode selection techniques for D2D communications that mainly aim at improving network throughput as well as spectrum efficiency @cite_1 . Optimal usage of the available radio resources is a well-known nonlinear NP-hard problem that becomes even more complex with the integration of D2D communications that underlay the cellular network @cite_10 . For this reason, a common practice has been to devise low-complexity, heuristic algorithmic solutions or to relax part of the constraints (e.g. power or resource block allocation related) and propose sub-optimal techniques for D2D-aware resource allocation @cite_10 , @cite_15 .
@cite_10 @cite_15 @cite_1
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The basic idea of device-to-device (D2D) communication is that pairs of suitably selected wireless devices reuse the cellular spectrum to establish direct communication links, provided that the adverse effects of D2D communication on cellular users are minimized and that cellular users are given higher priority in using limited wireless resources. Despite its great potential in terms of coverage and capacity performance, implementing this new concept poses some challenges, particularly with respect to radio resource management. The main challenges arise from a strong need for distributed D2D solutions that operate in the absence of precise channel and network knowledge. To address this challenge, this paper studies a resource allocation problem in a single-cell wireless network with multiple D2D users sharing the available radio frequency channels with cellular users. We consider a realistic scenario where the base station (BS) is provided with strictly limited channel knowledge, whereas D2D and cellular users have no information. We prove a lower bound for the cellular aggregate utility in the downlink with fixed BS power, which allows for decoupling the channel allocation and D2D power control problems. An efficient graph-theoretical approach is proposed to perform channel allocation, which offers flexibility with respect to allocation criteria (aggregate utility maximization, fairness, and quality-of-service (QoS) guarantee). We model the power control problem as a multiagent learning game. We show that the game is an exact potential game with noisy rewards, which is defined on a discrete strategy set, and characterize the set of Nash equilibria. Q-learning better-reply dynamics is then used to achieve equilibrium. Device-to-device (D2D) communication is developed as a new paradigm to enhance network performance according to LTE and WiMAX advanced standards. The D2D communication may have dedicated spectrum (overlay) or shared spectrum (underlay). However, the allocated dedicated spectrum may not be effectively used in the overlay mode, while interference between the D2D users and cellular users cause impairments in the underlay mode. Can the resource allocation of a D2D system be optimized using the cognitive approach where the D2D users opportunistically access the underutilized radio spectrum? That is the focus of this paper. In this paper, the transmission rate of the D2D users is optimized while simultaneously satisfying five sets of constraints related to power, interference, and data rate, modeling D2D users as cognitive secondary users. Furthermore, a two-stage approach is considered to allocate the radio resources efficiently. A new adaptive subcarrier allocation scheme is designed first, and then, a novel power allocation scheme is developed utilizing geometric water-filling approach that provides optimal solution with low computation complexity for this nonlinear problem. Numerical results show that the proposed approach achieved significant performance enhancement than the existing schemes. Device-to-device (D2D) communication has been recently proposed to mitigate the burden of base stations by leveraging the underutilized cellular spectrum resources, where high overall network throughput and D2D access rate are critical for its service performance and availability. In this paper, we study the resource allocation problem to push the limit of D2D communication underlaying cellular networks by allowing multiple D2D links to share resource with multiple cellular links. We propose FRESH, a f ull re source sh aring scheme where each subchannel can be shared by a cellular link and an arbitrary number of D2D links. In particular, FRESH first divides the communication links into so-called full resource sharing sets such that, within each set, all D2D link members are able to reuse the whole allocated resources. Thereafter, it allocates a sum of spectrum resources to each obtained full resource sharing set. As compared with state-of-the-art schemes, FRESH provides fine-grained resource allocation, resulting in throughput improvements of up to one order of magnitude, and D2D access rate improvements of up to 5 times with a moderate node density (e.g., on the order of 1 user per 400 square meters). In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.