Multiple D2D Pairs Research Articles

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

Interference‐aware feedback based iterative Hungarian approach to allocate multiple channels to multiple D2D pairs to maximize underlay D2D network capacity

SummaryThe spectral efficiency of a cellular network can be increased significantly by allowing spatial reuse of its spectrum by an underlay device‐to‐device (D2D) network. In an underlay D2D network, devices in close vicinity are allowed to establish low‐power direct links with little to no involvement of the base station. In order to increase the spectral efficiency and the number of devices with channel access, multiple D2D pairs may transmit in each cellular channel. Additionally, each pair can be allowed to utilize multiple channels to transmit so as to maximize the D2D network capacity. This multiple‐pair multiple‐channel (MPMC) strategy is quite appealing but is limited by the resultant additional aggregate interference and the inherent complexity, hence necessitating the need for a fast and reliable channel allocation scheme. This work proposes a polynomial‐time iterative Hungarian assignment with feedback (IHAF) algorithm for multiple channel allocations amongst multiple D2D pairs that increases the D2D network capacity manifold while maintaining the desired minimum capacity for each cellular user.

Energy Efficiency Optimization for SWIPT-Based D2D-Underlaid Cellular Networks Using Multiagent Deep Reinforcement Learning

In this article, we study the optimization of energy efficiency in wireless device-to-device (D2D-underlaid cellular networks where multiple D2D pairs adopt simultaneous wireless information and power transfer functionality. We formulate the optimization problem, which is a NP-hard combinatorial problem with nonlinear constraints. First, we use optimization-based-iterative techniques such as exhaustive search (ES) and gradient search (GS) with barrier, which are generally used to obtain the global optimum and local optimum of the nonconvex optimization problem, respectively. Considering that these techniques require a centralized unit to share information with each other, we propose multiagent deep reinforcement learning to solve this optimization problem in a distributed manner, which provides optimal decision making together with efficient deep network training under inequality constraints including transmit power, power splitting ratio, and minimum requirement data rate for D2D and cellular users. In this proposed method, we consider the virtual environment in which each agent can train their model according to shared information, and then, we deploy the trained model into the actual environment where each agent can only know their channel gain, interference power, and required minimum throughput. Simulation results show that the proposed algorithm can afford a near-global-optimum solution with much lower computation complexity than ES and outperforms the GS.

Interplay Between Interference-Aware Resource Allocation Algorithm Design, CSI, and Feedback in Underlay D2D Networks

A key problem in underlay device-to-device (D2D) systems is assigning cellular users and D2D users to subchannels to improve spatial reuse while controlling the interference they cause to each other. We present a unified treatment of this problem for two practically motivated partial and statistical channel state information (CSI) models with quantized feedback. They differ in the CSI available at the D2D receiver. In both models, the nodes only have statistical information of inter-D2D and inter-cell interferences, and employ fractional power control. We present two polynomial-time algorithms to assign multiple D2D pairs to subchannels, namely, relaxation-pruning algorithm (RPA) and cardinality-constrained subchannel assignment algorithm (CCSAA). RPA and CCSAA guarantee a D2D sum rate that is at least one-half and one-third, respectively, of the optimal sum rate. We also propose a novel statistical rate upgradation technique that exploits the allocation information to improve the D2D rates. We observe that inter-D2D interference has a more pronounced effect in the statistical CSI model. The algorithms respond differently to the two CSI models. RPA outperforms CCSAA in the partial CSI model, while CCSAA outperforms RPA in the statistical CSI model despite its weaker performance guarantee.

Dynamic Power Allocation in D2D Communications Using Deep Reinforcement Learning

Due to the development of communication technology, mobile devices continue to increase. As one of the critical technologies of 5G, D2D communication is an up-and-coming technology. In this paper, multiple D2D pairs usually multiplex the same channel, which will cause serious channel interference. To solve this problem, a distributed deep reinforcement learning framework is proposed, which can well adapt to the power allocation in a dynamic environment. The simulation results show that compared with other benchmark methods, the proposed scheme can improve the overall D2D rate.

Robust Design of Multicell D2D Communication Under Partial CSI

We consider device-to-device (D2D) communication underlaid in a cellular network to share the uplink resource of cellular users (CUs). It is a key the emerging Internet of Things to support vehicle-to-everything communication networks. In a multicell scenario, both D2D pairs and CUs may cause significant significant intercell interference (ICI) to the neighboring cells. Furthermore, due to substantial signaling overhead, we assume only partial channel state information (CSI) of D2D links at the base station. We consider joint power control, beamforming, and CU-D2D matching problem, assuming partial CSI from D2D pairs under the general Nakagami fading model. We formulate a joint receive beamforming and robust power control optimization problem for a CU-D2D pair to expected sum rate under the power budget, while meeting the minimum SINR requirements and worst case ICI limits at neighboring cells in a probabilistic sense. We propose an efficient algorithm that combines an iterative D2D feasibility check and a ratio-of-expectation approximation. A performance upper bound is also developed for benchmarking. For multiple CUs and D2D pairs, due to orthogonal channelization within each cell, we first focus on the problem of joint power control and beamforming for a CU-D2D pair and show how our proposed solution can be leveraged to find a solution for this general problem. The complexity analysis of the proposed approach is also provided. Simulation results show that the proposed algorithm gives performance close to the upper bound.

Reuse of Multiple Channels by Multiple D2D Pairs in Dedicated Mode: A Game Theoretic Approach

Device-to-device communication (D2D) is expected to accommodate high data rates and to increase the spectral efficiency of mobile networks. The D2D pairs can opportunistically exploit channels that are not allocated to conventional users in a dedicated mode. To increase the sum capacity of D2D pairs in the dedicated mode, we propose a novel solution that allows the reuse of multiple channels by multiple D2D pairs. In the first step, the bandwidth is split among D2D pairs so that each pair communicates at a single channel that guarantees a minimal capacity for each pair. Then, the channel reuse is facilitated via a grouping of the D2D pairs into coalitions. The D2D pairs within one coalition mutually reuse the channels of each other. We propose two approaches for the creation of the coalitions. The first approach reaches an upper-bound capacity by optimal coalitions determined by the dynamic programming. However, such approach is of a high complexity. Thus, we also introduce a low-complexity algorithm, based on the sequential bargaining, reaching a close-to-optimal capacity. Moreover, we also determine the transmission power allocated to each reused channel. Simulations show that the proposed solution triples the sum capacity of the state-of-the-art algorithm with the highest performance.

Distributed pricing‐based resource allocation for dense device‐to‐device communications in beyond 5G networks

AbstractConsidering the dramatic increase of data rate demand in beyond fifth generation (5G) networks due to the numerous transmitting nodes, dense device‐to‐device (D2D) communications where multiple D2D pairs can simultaneously reuse a cellular link can be considered as a communication paradigm for future wireless networks. Since distributed methods can be more practical compared to complex centralized schemes, we propose a low‐complexity distributed pricing‐based resource allocation algorithm to allocate power to cellular user equipments (CUEs) and D2D pairs constrained to the minimum quality‐of‐service (QoS) requirements of CUEs and D2D pairs in two phases. The price of each link is set by the base station (BS). CUEs and D2D pairs maximize their rate‐and‐interference‐dependent utility function by adjusting their power in the first phase, while the price of each link is updated at the BS based on the minimum QoS requirements of CUEs and D2D pairs in the second phase. The proposed utility function controls D2D admission, hence it is suitable for dense scenarios. The proposed method is fast due to the closed‐form solution for the first phase power allocation. Numerical results verify the effectiveness of the proposed method for practical dense beyond 5G scenarios by allocating resources to multiple D2D pairs and taking advantage of the spatial reuse gain of D2D pairs. Furthermore, the utility function definition is discussed to illustrate its effectiveness for SE maximization. Finally, the SE of the proposed method is compared to other centralized and distributed algorithms to demonstrate the higher sum‐rate performance of the proposed algorithm.

Joint Optimization of Spectral Efficiency and Energy Harvesting in D2D Networks Using Deep Neural Network

In this work, we study the joint optimization of energy harvesting and spectrum efficiency in wireless device-to-device (D2D) networks where multiple D2D pairs adopt simultaneous wireless information and power transfer (SWIPT) functionality with a power-splitting policy. To observe the trade-off relationship between spectrum efficiency and energy harvesting via SWIPT, we construct an objective function using the weighted sum method, which scalarizes the dominant with spectrum efficiency and energy harvesting, and attempt to find the optimal transmit power and power-splitting ratio to maximize the objective function. Typical iterative search algorithms such as exhaustive search (ES) or gradient search (GS) with a log barrier function are employed to find the global optimum and sub-optimum, respectively. Furthermore, we apply a deep neural network (DNN) learning algorithm to deal with the non-convexity of the objective function with an effective loss function. The simulation results verify the trade-off relationship between spectrum efficiency and energy harvesting, and show that the DNN-based algorithm can achieve a near-global optimal solution with computational complexity much lower than that of the optimization-based iterative algorithms.

Enabling Device‐to‐Device (D2D) Communication for the Next Generation WLAN

Device‐to‐device (D2D) communication technology is widely acknowledged as an emerging candidate to alleviate the wireless traffic explosion problem for the next generation wireless local area network (WLAN) IEEE 802.11ax. In this paper, we integrate D2D communication into IEEE 802.11ax for maximizing the spectrum efficiency. Due to the spectrum scarcity, the number of available resource units (RUs) is typically less than D2D pairs and stations (STAs), which makes the management of spectrum resources more complex. To tackle this issue, we design an efficient resource management algorithm for uplink random access and resource allocation in D2D‐enabled 802.11ax, which provides more channel access and resource reuse opportunities for STAs and D2D pairs. Specifically, we propose an enhanced back‐off mechanism and derive the optimal contention window (CW) by a theoretical model, which improves the uplink random access efficiency. To tackle the complex interference problem in RU scheduling phase, we develop an efficient and low‐complexity resource allocation algorithm based on the maximal independent set (MIS), which can schedule multiple D2D pairs to share the same RU with the specific STA. Simulation results demonstrate that the proposed algorithm significantly improves the network performance in terms of system throughput, collision rate, complete time, and channel utilization.

Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC

Mission-critical communication (MCC) is one of the main goals in 5G, which can leverage multiple device-to-device (D2D) connections to enhance reliability for mission-critical communication. In MCC, D2D users can reuses the non-orthogonal wireless resources of cellular users without a base station (BS). Meanwhile, the D2D users will generate co-channel interference to cellular users and hence affect their quality-of-service (QoS). To comprehensively improve the user experience, we proposed a novel approach, which embraces resource allocation and power control along with Deep Reinforcement Learning (DRL). In this paper, multiple procedures are carefully designed to assist in developing our proposal. As a starter, a scenario with multiple D2D pairs and cellular users in a cell will be modeled; followed by the analysis of issues pertaining to resource allocation and power control as well as the formulation of our optimization goal; and finally, a DRL method based on spectrum allocation strategy will be created, which can ensure D2D users to obtain the sufficient resource for their QoS improvement. With the resource data provided, which D2D users capture by interacting with surroundings, the DRL method can help the D2D users autonomously selecting an available channel and power to maximize system capacity and spectrum efficiency while minimizing interference to cellular users. Experimental results show that our learning method performs well to improve resource allocation and power control significantly.

Deep Reinforcement Learning for Joint Channel Selection and Power Control in D2D Networks

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm.

Deep Multiagent Reinforcement-Learning-Based Resource Allocation for Internet of Controllable Things

Ultrareliable and low-latency communication (URLLC) is a prerequisite for the successful implementation of the Internet of Controllable Things. In this article, we investigate the potential of deep reinforcement learning (DRL) for joint subcarrier-power allocation to achieve low latency and high reliability in a general form of device-to-device (D2D) networks, where each subcarrier can be allocated to multiple D2D pairs and each D2D pair is permitted to utilize multiple subcarriers. We first formulate the above problem as a Markov decision process and then propose a double deep $Q$ -network (DQN)-based resource allocation algorithm to learn the optimal policy in the absence of full instantaneous channel state information (CSI). Specifically, each D2D pair acts as a learning agent that adjusts its own subcarrier-power allocation strategy iteratively through interactions with the operating environment in a trial-and-error fashion. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in real time. It is worth mentioning that the proposed algorithm is especially suitable for cases where the environmental dynamics are not accurate and the CSI delay cannot be ignored.

Physical Layer Security in D2D Underlay Cellular Networks With Poisson Cluster Process

Device-to-device (D2D) communication is a promising solution to meet rapidly growing demands for data services via spectrum reuse. This paper studies the physical layer security in a D2D underlay cellular network from a network-wide perspective, where the locations of D2D and cellular users are modeled as Poisson cluster processes (PCPs) to characterize the clustering feature of D2D users, the locations of eavesdroppers (Eves) and base stations (BSs) are modeled as a PCP and Poisson point process (PPP), respectively. We establish an analytical framework to assess the coverage and security performance of the network. Two scenarios are considered, i.e., one D2D pair scenario and multiple D2D pairs scenario, where in each cell there is one or multiple D2D users (DUs) sharing the frequency spectrum with the cellular users (CUs) in each time slot of the TDMA scheme adopted by BSs. In each considered scenario, we derive exact expressions for the coverage outage probabilities (COPs) and secrecy outage probabilities (SOPs), respectively, for both the CUs and DUs. Furthermore, the exact expression for the network-wide secrecy throughput (ST) is derived. Numerical results are presented to verify our theoretical derivations and reveal some insights into the impact of various parameters on the system performance.

Cluster oriented resource allocation and power optimisation for D2D network in cellular communications

This work proposes a cluster oriented channel assignment and difference of two convex functions (d.c.) programming based power optimisation algorithm for the downlink device-to-device (D2D) communication underlaying cellular networks. The authors' objective is to maximise D2D throughput while protecting the performance of existing cellular users (CUs) whose channel is reused among multiple D2D pairs, by imposing a minimum rate requirement constraint on each CU. The joint channel and power optimisation problem is a mixed integer non-linear programming problem that is NP-hard to solve. Therefore, a three stage solution is proposed: cluster formation to minimise the interference among the D2D pairs followed by an optimal channel assignment using the Hungarian algorithm and then an iterative power optimisation algorithm based on d.c. programming. Moreover, the transmission power of base station and D2D users is optimised on the same channel while considering the mutual interference among D2D pairs. Numerical results verify the effectiveness of the proposed resource allocation scheme in terms of the D2D sum rate and the number of successful transmission of D2D users. Moreover, the iterative power optimisation algorithm shows a fast convergence behaviour. In addition to this, the energy efficiency is also analysed with respect to various parameters.

On Data Dissemination Enhanced by Network Coded Device-to-Device Communications

Data dissemination to multiple users within a predetermined deadline is a commonly required function in many emerging wireless scenarios. In this paper, we propose a data dissemination scheme enhanced by device-to-device (D2D) communications and random linear network coding (RLNC), where the broadcast and the D2D links are used simultaneously with on-the-fly RLNC to accelerate the dissemination. The probability distribution of the completion time of using either pure or systematic RLNC is analyzed assuming a finite field size, under a general model where the cooperation may occur probabilistically and multiple packets may be exchanged between the D2D users in each time slot. An optimization problem is formulated to design the cooperation parameters to minimize the expected energy consumption of the system. Numerical and simulation results show that the analysis is accurate, and that to minimize the energy consumption a tradeoff exists between allowing for longer broadcast and for more intensive cooperation. The cooperation parameters designed for multiple D2D pairs are shown to be effective for meeting the reliability requirements.

Coalition Game-Based Strategy for Resource Allocation and Transmit Power Control in D2D Communication

Resource allocation and co-channel interference are the two major technical challenges in the implementation of device-to-device communication technology. We propose an innovative resource allocation strategy along with dynamic power control for D2D communication. The resource allocation problem is formulated as a single utility function for multiple D2D pairs and cellular users in underlaying cellular networks. The proposed strategy based on overlapping coalition formation game allows a D2D pair to reuse multiple resource blocks. It aims to maximize the system sum rate maintaining minimal adverse impact of the interference on the overall system performance. Numerical results obtained through computer simulation show that the proposed strategy significantly improves the system sum rate along with reduced power consumption.

Resource Allocation for D2D Communication With Multiple D2D Pairs Reusing Multiple Channels

In this letter, the goal is to maximize sum capacity of device-to-device (D2D) communication through a reuse of each radio channel by multiple D2D pairs while each D2D pair can access multiple channels. Since existing approaches cannot be easily extended to enable reuse of multiple channels by multiple D2D pairs in scenario with a high interference among the D2D pairs, we propose a novel resource allocation consisting of two phases. In an initial phase, all available channels are assigned by the Hungarian algorithm so that each channel is occupied by just one D2D pair. In a reuse phase, multiple D2D pairs are sequentially added to the individual channels according to their priority expressed by channel quality and received interference from already added D2D pairs. The proposal significantly outperforms existing solutions and reaches close to theoretical upper bound capacity despite a very low complexity of the proposed algorithm.

Learning for Matching Game in Cooperative D2D Communication With Incomplete Information

This paper considers a cooperative device-to-device (D2D) communication system, where the D2D transmitters (DTs) act as relays to assist cellular users (CUs) in exchange for the opportunities to use licensed spectrum. Based on the interaction of each D2D pair and each CU, we formulate the pairing problem between multiple CUs and multiple D2D pairs as a one-to-one matching game. Unlike most existing works, we consider a realistic scenario with incomplete channel information. Thus, each CU lacks enough information to establish its preference over D2D pairs. Therefore, traditional matching algorithms are not suitable for our scenario. To this end, we convert the matching game to an equivalent non-cooperative game, and then propose a novel learning algorithm, which converges to a stable matching.

Multiple device-to-device users underlaying downlink cellular networks with superposition coding

Device-to-Device (D2D) communications has been proposed to provide high data rate service via direct transmissions between devices. Cooperation between the cellular user (CU) and the D2D user can be achieved using superposition coding, where the D2D transmitter (DT) allocates some of its transmission power to forward the CU’s traffic, and transmits to its own D2D receiver (DR) with the remaining power. The sum rate of the cellular and D2D networks in existing schemes are limited by allowing only a single pair of D2D users underlaying the cellular network. In this paper, we propose several new spectrum access policies involving multiple D2D pairs. Despite the additional interference, it is shown that both the CU and the individual D2D users’ achievable rates can be improved simultaneously by adjusting the power allocation factor in the superposition coding, resulting in a significant gain in the sum rate when the number of D2D pairs are below a threshold. This threshold is quantified using second order approximations for the average achievable rates for both the CU and the individual D2D users for different policies.