Resource Allocation For D2D Communications Research Articles

Socially Aware Secrecy-Ensured Resource Allocation in D2D Underlay Communication: An Overlapping Coalitional Game Scheme

With the popularity of proximity-based services, device-to-device (D2D) communication underlaying cellular networks is a promising technology to cope with the growing demands by improving network resource utilization. However, the wireless communication's broadcast nature is vulnerable to eavesdropping, and thus, ensuring a secrecy communication for both cellular user equipments (CUEs) and D2D pairs in an underlay network is a challenging issue. We investigate the problem of physical-layer secure transmission jointly with resource allocation in D2D communications. Different from existing works, we framed overlapping (partial) coalitional game where each D2D pair can access multiple CUEs' spectral resources. Moreover, the multiple D2D pairs can share single CUE subchannel in multiple eavesdroppers scenario to ensure information security for both CUEs and D2D pairs and to maximize system sum rate in a socially aware D2D network. We incorporate the mutual interference and propose different transmission modes for a secrecy-ensured resource allocation-based overlapping coalition formation scheme with transferable utility to obtain a final stable partition. We further prove the proposed algorithm stability, convergence, and computational complexity. Both analytical and numerical results demonstrate the effectiveness of our proposed scheme, which ensures a system-wide security and at the same time improves the performance by maximizing the system sum rate.

Resource Allocation for D2D Communications Underlaying a NOMA-Based Cellular Network

This letter investigates the power control and channel assignment problem in device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network. With the successive interference cancellation decoding order constraints, our target is to maximize the sum rate of D2D pairs while guaranteeing the minimum rate requirements of NOMA-based cellular users. Specifically, the optimal conditions for power control of cellular users on each subchannel are derived first. Then, based on these results, we propose a dual-based iterative algorithm to solve the resource allocation problem. Simulation results validate the superiority of proposed resource allocation algorithm over the existing orthogonal multiple access scheme.

Distributed Resource Allocation for D2D Communications Underlaying Cellular Networks in Time-Varying Environment

In this letter, we address joint channel and power allocation in a device-to-device (D2D) network underlaying a cellular network in a time-varying environment. A fully distributed solution, which does not require information exchange, is proposed to allocate channel and power levels to D2D pairs while ensuring the quality of service (QoS) of the cellular user equipments (CUEs). The problem is modeled as a Stackelberg game with pricing. At the leader level, base station sets prices for the channels to ensure the QoS of the CUEs. At the follower level, D2D pairs use an uncoupled stochastic learning algorithm to learn the channel indices and power levels while minimizing the weighted aggregate interference and the price paid. The follower game is shown to be an ordinal potential game. We perform simulations to study the convergence of the algorithm.

Increasing efficiency of resource allocation for D2D communication in NB-IoT context

Internet of things (IoT) and device to device (D2D) communications are among the novel promising technologies in the current releases of 4G and they will play a fundamental role in the next generation 5G as well. In this paper, it is investigated the impact of allocation strategies that take into account the mutual interference in D2D Narrow-Band IoT terminals and cellular terminals transmitting in the same resource block. In a multi-cellular downlink context, the proposed approach and the analysis can serve also as an efficient criterion for selecting the target SINR, useful for managing the power control in the uplink. The rate improvement, measured with the proposed approach, is between 10% and 15% w.r.t. conventional techniques.

Overlapping Coalition Formation Games for Joint Interference Management and Resource Allocation in D2D Communications

In this paper, an overlapping coalition formation game is proposed to conduct joint interference management and resource allocation in device-to-device (D2D) communications. We aim at improving the sum rate of all D2D links in D2D communications underlying cellular network without sacrificing the quality of service of each cellular user (CU) and D2D link. First, based on the cross-tier interference level between CUs and D2D links, each D2D link selects multiple best resource blocks (RBs) to reuse. Then, by dividing the user equipments (i.e., CU and D2D links) which share the same RB into one coalition, an initial overlapping coalitional structure is formed. A cooperative game with overlapping coalitions is further proposed to maximize the system utility in terms of sum rate of all D2D links. Finally, the characteristic analysis of the proposed algorithm is provided to validate that the user equipments can cooperate to form a stable and most beneficial coalitional structure with overlapping coalitions. Simulation results show that the proposed overlapping coalition formation game achieves a better performance compared with other existing schemes.

A scalable data-plane architecture for one-to-one device-to-device communications in LTE-Advanced

One-to-one device-to-device (D2D) communications are expected to play a major role in future releases of LTE-A, as well as in future 5G networks. Despite the abundance of works on resource allocation for D2D communications, few works, if any, discuss how D2D should be realized within the LTE-A protocol stack. While it is generally understood that D2D endpoints should be able to communicate both on the direct path or sidelink (SL) and on the relayed path (RP) through the eNB, little has been said on how this can be achieved in practice. In this paper we present a comprehensive proposal for a data-plane architecture for D2D communication: we define how communications should occur on the SL and the RP, and propose a solution for the challenges associated with mode switching between the SL and the RP. In particular, we argue that two different communication modes on the RP are required to allow D2D connections to be kept alive across cell borders in a multicell environment. Our proposal is scalable, since it does not require any signaling, and is guaranteed to not introduce losses. We evaluate our proposal through detailed system-level simulations, also focusing on its interplay with transport-layer protocols.

Maximizing throughput gain via resource allocation in D2D communications

By reusing the cellular resources, device-to-device (D2D) communication is becoming a very promising technology that greatly enhances the spectrum utilization. To harvest the benefits that D2D communications can offer, efficient resource allocation strategy is required to guarantee the demands of quality of service (QoS) for both cellular and D2D users. This paper proposes a resource allocation scheme to alleviate the performance deterioration of the D2D communications with spectrum reuse. To maximize the overall throughput gain, the proposed scheme is designed to reduce the rate loss of cellular users and improve the rate of D2D users simultaneously in a two-step manner. Specifically, it first calculates the reuse gain for a single D2D pair and a single cellular user. Next, a maximum weight bipartite matching is further proposed to select the reuse pair to maximize the overall network throughput gain. Numerical results demonstrate that the proposed resource allocation scheme can significantly improve the network throughput performance with average user rate guaranteed.

Learning‐based resource allocation in D2D communications with QoS and fairness considerations

AbstractIn device‐to‐device (D2D) communications, D2D users establish a direct link by utilizing the cellular users' spectrum to increase the network spectral efficiency. However, due to the higher priority of cellular users, interference imposed by D2D users to cellular ones should be controlled by channel and power allocation algorithms. Due to the unknown distribution of dynamic channel parameters, learning‐based resource allocation algorithms work more efficient than classic optimization methods. In this paper, the problem of the joint channel and power allocation for D2D users in realistic scenarios is formulated as an interactive learning problem, where the channel state information of selected channels is unknown to the decision center and learned during the allocation process. In order to achieve the maximum reward function by choosing an action (channel and power level) for each D2D pair, a recency‐based Q‐learning method is introduced to find the best channel‐power for each D2D pair. The proposed method is shown to achieve logarithmic regret function asymptotically, which makes it an order optimal policy, and it converges to the stable equilibrium solution. The simulation results confirm that the proposed method achieves better responses in terms of network sum rate and fairness criterion in comparison with conventional learning methods and random allocation.

DTMR: delay-aware traffic admission, mode selection, and resource allocation for D2D communication

In device-to-device (D2D) communication underlaying a cellular network, the resource control involves traffic admission, mode selection, orthogonal channel assignment, and power control. Traffic admission limits queueing delay, mode selection exploits the proximity gain, and resource allocation guarantees user performance. Jointly optimizing these factors is highly challenging due to the stochastic nature of the system and the coupled control actions. Many previous works only consider a subset of these factors. In this paper, we tackle the joint optimization problem for delay-aware D2D communication. In particular, considering both dynamic traffic arrival and time-varying channel fading, we aim to maximize the time-average sum-rate of the network subject to the time-average throughput guarantee of users and resource allocation constraints. Thus, through presenting a Lyapunov optimization framework, we design an optimal delay-aware traffic admission, mode selection, and resource allocation (DTMR) strategy with polynomial time complexity based on dual optimization and ellipsoid search. We also analytically derive lower bound of the time-average sum-rate achieved by the proposed DTMR strategy. Further, we develop a fast heuristic strategy by decoupling the binary constraints of mode selection and channel assignment. Finally, simulation results demonstrate the superiority of the DTMR strategy against alternative strategies.

Social-Aware Resource Allocation and Optimization for D2D Communication

The undiminished growth of research activities to converge social awareness with D2D communication has paved the way for facilitating and providing significant benefits to users. Realizing these benefits depends on efficiently addressing several main technical challenges associated with the convergence. Although there are many research studies related to social networks and D2D communication, convergence of these two areas leads to further research efforts to implement social-aware D2D communication. In this article, we discuss recent advances in the domain of D2D communication from the perspective of social-aware resource allocation and optimization. We also categorize and classify the literature by devising a taxonomy based on channel-centric attributes, objectives, solving approaches, networking technologies, characteristics, and communication types. Moreover, we also outline the key requirements with the aim of providing guidelines for the domain researchers and designers to enable the social-aware resource allocation for D2D communication. Several open research challenges are presented as future research directions.

Energy-Efficient Resource Allocation for D2D Communications in Cellular Networks

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm.

Social-Community-Aware Resource Allocation for D2D Communications Underlaying Cellular Networks

Device-to-device (D2D) communication has been proposed as a promising technology for future cellular communication systems due to its advantages of high spectrum efficiency, low energy consumption, and enhanced system capacity. Resource allocation for D2D communications, which occupies nonorthogonal channels with cellular transmissions, is an important problem in terms of achieving the aforementioned benefits. In this problem, there are two fundamental challenges to be addressed: 1) how to emulate cellular users to share their resources and 2) how to efficiently allocate resources in terms of channels for D2D pairs. In this paper, we exploit social ties in human-formed social networks to enhance D2D resource sharing and further propose a social-community-aware D2D resource allocation framework, where cellular users would like to share their channels with D2D communications in the same community formed by a group of people with close social ties. After that, we propose a two-step coalition game, where a coalition formulation is established for communities, and an optimal resource allocation problem is formulated for D2D pairs. Extensive simulations on random networks and real mobile trace verify the effectiveness of the proposed scheme.

Mixed Mode Transmission and Resource Allocation for D2D Communication

In cellular communication systems with optional device-to-device (D2D) links, user equipments (UEs) can operate in either D2D mode or cellular mode for data transport. This work introduces mixed-mode D2D communication in which D2D links can operate in multiple modes through resource multiplexing. Within this framework, we study the problem of maximizing weighted D2D sum rate under cellular rate constraints by optimizing mixed-mode allocation and resource allocation in term of transmit power and subchannel assignment. Due to nonconvex cellular rate constraints and binary constraints of subchannel allocation, this problem is a nonconvex mixed-integer problem that is generally difficult to solve. We propose a two-step approach by introducing energy-splitting variables such that mixed-mode allocation and resource allocation can be decoupled and optimized independently. The resulting algorithm can be distributive, requires little signaling overhead, and has low computational complexity. We present extensive numerical results to demonstrate the practicality of our proposed algorithm with regard to various network parameters.

Mode Selection and Resource Allocation in Device-to-Device Communications With User Arrivals and Departures

The pervasive increasing mobile devices and explosively increasing data traffic pose imminent challenges on wireless network design. Device-to-device (D2D) communication is envisioned to play a key role in the fifth generation cellular networks to efficiently support much larger and more diverse set of devices. This paper investigates the mode selection and resource allocation for D2D communications with dynamic user arrivals and departures. We formulate the optimal resource control problem to minimize the average energy consumption of flow transmission into an infinite horizon average reward Markov decision process. In order to deal with the well-known curse of dimensionality problem and facilitate distributed implementation, we approximate the mode selection $Q$ -factor by the sum of per-queue mode selection $Q$ -factors. Moreover, we apply distributive stochastic online learning to estimate the per-queue $Q$ -factors. Simulation results show that the proposed approach outperforms various existing baseline algorithms.

Resource Allocation for Device-to-Device Communications Reusing Uplink in Cellular Networks

Efficient spectrum sharing is an important issue in Device-to-Device (D2D) communications underlaying cellular networks as it can mitigate the interference to cellular users and improve the performance of the systems. In this paper, we formulate the radio resource allocation in D2D communications as a mixed nonlinear integer programing. We show the formulated problem is NP-hard and thus a polynomial time algorithm to solve is not possible. Since such a problem is very hard to obtain the optimal solution within a short running time, we instead propose a fast heuristic suboptimal algorithm to mitigate the interference caused to cellular users and improve the performance of the systems. Simulation results are provided to evaluate the performance of the proposed algorithm.

Social-Aware Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks

The ever-increasing demands for local area services underlaying cellular networks benefit from direct device-to-device (D2D) communications, where an efficient scheme for resource allocation is needed to increase the system capacity as the result of interference caused by spectrum sharing. Current works mainly focus on maximizing the overall transmission capacity according to interference constraints of the physical domain. However, D2D users in the social domain form different social communities, and each social community is likely to improve its own group's data transmission cooperatively without considering other communities. Therefore, social relationships among mobile users influence the strategy of the resource allocations for the D2D communications. In this paper, we first introduce social relationships in the continuum space into the resource allocation for D2D communications, which consider the complex social connections in the social domain. Then a social group utility maximization game is formulated to maximize the social group utility of each D2D user, which quantitatively measures the joint performance of social and physical domains. We theoretically investigate the Nash Equilibrium of our proposed game and further propose a distributed algorithm based on the switch operations of the resource allocation vector. Numerical results demonstrate that our proposed solution increases the utility of overall social groups about 45% on average without loss of the fairness compared with other state-of-the-art schemes.

Joint User Association and Resource Allocation of Device-to-Device Communication in Small Cell Networks

With the recent popularity of smart terminals, the demand for high-data-rate transmission is growing rapidly, which brings a new challenge for the traditional cellular networks. Both device-to-device (D2D) communication and small cells are effective to improve the transmission efficiency of local communication. In this paper, we apply D2D communication into a small cell network system (SNets) and study about the optimization problem of resource allocation for D2D communication. The optimization problemincludes system scheduling and resource allocation, which is exponentially complex and the optimal solution is infeasible to achieve. Therefore, in this paper, the optimization problem is decomposed into several smaller problems and a hierarchical scheme is proposed to obtain the solution. The proposed hierarchical scheme consists of three steps: D2D communication groups formation, the estimation of sub-channels needed by each D2D communication group and specific resource allocation. From numerical simulation results, we find that the proposed resource allocation scheme is effective in improving the spectral efficiency and reducing the outage probability of D2D communication.

An Iterative Hungarian Method to Joint Relay Selection and Resource Allocation for D2D Communications

The joint relay selection and related subchannel and power allocation problem is investigated for relay-aided device-to-device (D2D) communications underlying cellular networks. We show the optimal power allocation problem can be solved in a closed-form. Considering that the associated relay selection and subchannel assignment problem is NP-complete, we devise an iterative technique, the iterative Hungarian method (IHM). Interestingly, numerical results show that the proposed technique can offer near-optimal performance with polynomial complexity.

Graph-Based Resource Allocation for D2D Communications Underlying Cellular Networks in Multiuser Scenario

With the characteristics of considerable user equipment, massive traffic and numerous local services, but limited frequency resources, the mobile communications in the future require efficient use of frequency resources. Device-to-device (D2D) communications underlying cellular networks have been considered as a promising solution to improve the resources utilization in local scenario. In this paper, we investigated the resources allocation problem in multiuser scenario and proposed a graph-based resources allocation scheme which can achieve suboptimal performance but with low computational complexity and less feedback information.