Resource Allocation For D2D Communications Research Articles

Optimizing resource allocation for D2D communications with incomplete CSI

Optimizing resource allocation for D2D communications with incomplete CSI

Mode Selection and Resource Allocation for D2D Communications With Tradeoff of Throughput and User Satisfaction

Mode Selection and Resource Allocation for D2D Communications With Tradeoff of Throughput and User Satisfaction

Performance of Q-learning based resource allocation for D2D communications in heterogeneous networks

This study investigates energy efficiency issues of device-to-device (D2D) communications in heterogeneous networks. To minimize the total transmitted power, an approach based on Q-learning together with adaptive ɛ-greedy is proposed to optimize the connection of user equipment (UE) with base station (BS) or access point (AP). The proposed adaptive ɛ-greedy can conduct the adequate exploration and exploitation operations for effective optimization. Simulation results indicate that in the single-cell scenario, the proposed method can attain performance close to the best solution.

Resource Allocation for D2D Communication in Multiservice Cellular Network

Allowing different services with different requirements to coexist is a hot topic for future communication. In order to meet the different communication requirements of users, this paper proposes a cellular communication system based on filtered orthogonal frequency division multiplexing (filtered-OFDM) supporting device-to-device (D2D) communication. Further, in order to improve the system performance, we study the user resource allocation problem in the system and propose a dynamic iterative resource allocation algorithm based on Hungarian algorithm (DRABH). The algorithm allocates resource blocks for D2D user pairs and all cellular users in the cell under the constraint of the lowest threshold of signal to interference plus noise ratio (SINR), and cellular users have high priority. Under the condition of ensuring that all cellular users can communicate, resource blocks are allocated to as many D2D user pairs as possible to maximize the number of user links in the network. Simulation results show that considering the interference introduced by filtered-OFDM and D2D communication, the proposed resource allocation scheme can significantly improve the number of user links.

Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective

Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-Multiple-Access (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication.

Multi-Agent Deep Reinforcement Learning-Based Power Control and Resource Allocation for D2D Communications

Device-to-device (D2D) communications are envisioned as a critical technology to support future ubiquitous mobile communications applications. However, the requirements of high mobility and low latency seriously constrain the performance improvement of D2D communications. In this letter, we investigate a deep reinforcement learning (DRL) based scheme of power and resource allocation for maximizing the throughput of D2D users (DUEs) and cellular users (CUEs). Based on DRL theory, D2D pairs are defined as distributed multiple agents, which aim to adaptively select the transmission power and resources to ease the co-channel interference without any prior information. Furthermore, a priority sampling based dueling double deep Q-network (PS-D3QN) distributed algorithm is proposed to help agents to learn the predominant features. Simulation results show that the proposed algorithm has higher throughput than the existing DRL algorithms with a strict delay constraint. Particularly, the probability that the agent selects high power decreases with the increase of remaining transmission time, which proves the agents effectively learn and dynamically sense the impacts of the delay constraint.

Enhanced Resource Allocation in D2D Communications With NOMA and Unlicensed Spectrum

Device-to-device (D2D) communications with non- orthogonal multiple access (NOMA) have been proved in enhancing the network capacity and reducing the burden of the traditional cellular network. Due to reusing the same resources, the underlying D2D communication may cause high interference to cellular users, and thus reducing the network throughput. To alleviate this issue, we consider moving D2D users to the unlicensed band. In this case, D2D users can reuse the resources in cellular networks or share the unlicensed band with WiFi users. However, a critical question is how to effectively perform NOMA user clustering and determine D2D pairs that should use unlicensed band and how to guarantee the quality service of users at both networks. Our objective is to maximize the network throughput by investigating a joint resource allocation problem of user clustering, power control, and D2D mode selection. Since the formulated problem is a mixed-integer nonlinear programming problem, finding an optimal solution is a great challenge. To solve this problem, we propose to use a swarm intelligence approach, namely whale optimization algorithm in solving our optimization problem with an efficient solution. Through simulation results, we show that our proposed scheme attains a better performance than other alternative schemes.

Distributed resource allocation for D2D communications underlaying cellular network based on Stackelberg game

With the development of artificial intelligence, the large-scale access of intelligence equipment with complex heterogeneity will bring unpredictable spectrum limitation and complex interferences to traditional cellular networks. This situation can be alleviated by device-to-device (D2D) technique which improves spectrum efficiency by reusing cellular resources. In this paper, a resource allocation framework comprising channel allocation and power control based on Stackelberg game is proposed for distributed interference coordination between D2D and cellular communications with quality-of-service guarantee. First, base station matches the channel to D2D pairs on the basis of potential throughput gain. Second, interferences from D2D pairs to cellular users are converted into a penalty for D2D pairs through the interlayer price, and the optimization problem of system throughput is decoupled into multiple subproblems those can be solved in distributed and iterative manner in each D2D pair. Simulation results show that two proposed distributed algorithms of channel allocation and power control on interference coordination perform well in convergence and overall system throughput.

Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.

User Clustering and Optimized Power Allocation for D2D Communications at mmWave Underlaying MIMO-NOMA Cellular Networks

Fifth-generation (5G) cellular networks are being developed to meet the ever-growing data traffic across mobile devices and their applications. The core of 5G cellular networks is leveraging wider and higher frequencies available at millimeter wave frequency (mmWave) bands, thus providing very high data rates for mobile devices. Multi-input multi-output (MIMO) is an essential technology for overcoming the high propagation loss at mmWave communications. In non-orthogonal multiple access (NOMA), multiple cellular user equipments (CUEs) communicate over the same time-frequency resources using a multiplexed power domain. In device-to-device (D2D) communications, two D2D user equipments (DUEs) communicate without passing through the base station. In the underlaying scenario, DUEs reuse the frequency resources allocated to CUEs for spectrum utilization but DUEs cause interferences for cellular and D2D communications. Integrating D2D communications with other 5G technologies has great potential for spectral efficiency improvement. Unfortunately, interference management and resource allocation are becoming increasingly challenging due to aggressive frequency reuse. In this paper, D2D communications at mmWave underlaying MIMO-NOMA cellular network system model is developed. Consequently, a novel resource allocation for D2D communications underlaying MIMO-NOMA cellular network is proposed. A resource allocation optimization problem is formulated for spectral efficiency maximization. To solve this NP-hard problem, the problem is decomposed into three subproblems: interference-aware graph-based user clustering, MIMO-NOMA beamforming design, and optimized power allocation based on particle swarm optimization. Simulation results demonstrate that the proposed algorithm for D2D communications at mmWave underlaying MIMO-NOMA cellular network delivers a greater spectral efficiency compared to the conventional D2D communications that operate underlay MIMO-orthogonal multiple access cellular networks.

Resource Allocation for D2D Communication in Cellular Networks Based on Stochastic Geometry and Graph-coloring Theory

In a device-to-device (D2D) underlaid cellular network, there exist two types of co-channel interference. One type is inter-layer interference caused by spectrum reuse between D2D transmitters and cellular users (CUEs). Another type is intra-layer interference caused by spectrum sharing among D2D pairs. To mitigate the inter-layer interference, we first derive the interference limited area (ILA) to protect the coverage probability of cellular users by modeling D2D users’ location as a Poisson point process, where a D2D transmitter is allowed to reuse the spectrum of the CUE only if the D2D transmitter is outside the ILA of the CUE. To coordinate the intra-layer interference, the spectrum sharing criterion of D2D pairs is derived based on the (signal-to-interference ratio) SIR requirement of D2D communication. Based on this criterion, D2D pairs are allowed to share the spectrum when one D2D pair is far from another sufficiently. Furthermore, to maximize the energy efficiency of the system, a resource allocation scheme is proposed according to weighted graph coloring theory and the proposed ILA restriction. Simulation results show that our proposed scheme provides significant performance gains over the conventional scheme and the random allocation scheme.

Emperor based resource allocation for D2D communication and QoF based routing over cellular V2X in urban environment (ERA-D2Q)

Cellular vehicle-to-everything (C-V2X) communications have elicited significant scrutiny in recent years owing to amenities such as comfort, efficiency, and an enhanced line-of-sight through the exchange of statistics between vehicles and other entities. In general, achieving less interference in terms of resource allocation and a higher quality of forwarding (QoF) during routing is a major problem owing to the highly mobile environment used in such communications. To overcome this bottleneck, the present study focuses on emperor-based resource allocation for device-to-device (D2D) communications and QoF routing (ERA-D2Q) in C-V2X. Our ERA-D2Q offers three sub-sequential processes: (1) coalition-based routing, (2) D2D communications and SignRank oriented pedestrian selection, and (3) emperor based resource allocation. To achieve QoF routing, ERA-D2Q introduces coalition game theory (CGT), which elects an optimum relay and reduces the amount of time for a packet transmission. Our D2D communication is applied in two different cases: (1) devices discovered within the coverage of the discoverer and (2) devices discovered out of the coverage of the discoverer. The second case is conducted with the aid of the best pedestrian, who is elected using the SignRank algorithm. The best pedestrian sends a relay request to the 5G base station (BS) to discover a destination for D2D communications. For this purpose, 5G BS utilizes the Mamdani interval type 2 fuzzy algorithm. To optimize the resource blocks assigned to the D2D communications, an emperor penguin colony algorithm is established for the proposed ERA-D2Q, which is employed in an OMNET++ simulator. Finally, we validate the performance achieved in our study using five metrics, namely, the packet delivery ratio (PDR), average transmission delay (ATD), throughput, mean opinion score (MoS), and jitter. The evaluation results prove that our ERA-D2Q enhances the PDR and throughput by up to 35%, and decreases the ATD by up to 48%, the jitter by up to 33%, and the MoS by 40% compared to the existing methods QFRG, CF, SMRS, SRA, and DTDMD.

Online and robust resource allocation for D2D communications assisted by Green relays

Device-to-device (D2D) communications in cellular networks allow proximity users to communicate directly without going through the base station. Concentrating on scenarios where the direct communication between potential D2D users may not be reliable due to poor proximity and link condition, and paying attention to energy harvesting wireless communications, this study focuses on the green relay-assisted D2D communications, where long-term evolution-advanced layer-3 relays powered by renewable energy are employed. To achieve the trade-off between system capacity maximisation and user fairness, the authors formulate an offline resource block (RB) and power allocation problem under imperfect channel information with the objective of maximising the weighted sum-rate of all users. Then, based on battery dynamics, the original problem is reformulated as an online one with energy outage avoidance. To obtain distributed solutions, the online problem is further transformed into a convex one, and the rate outage-probability requirement is also converted for tractability. Finally, applying Lagrangian duality, the distributed RB and power allocation is derived, which is globally optimal to the reformulated online problem. Comparing with a baseline scheme where all users communicate directly without assistance of relays, numerical results show the superiority of the proposed approach in terms of the average achievable data rate.

Energy‐efficient resource allocation in D2D communications for energy harvesting–enabled NOMA‐based cellular networks

SummaryIn this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.

English

English

Secrecy Ensured Socially Aware Resource Allocation in Device-to-Device Communications Underlaying HetNet

Device-to-device (D2D) communication standardization is underway and considered as one of the key technologies in the 5G ecosystem. Eavesdropping is a well-known security risk for D2D communications. Thus, ensuring information security for both cellular users (CUs) and D2D pairs in an underlay network is quite challenging. In this paper, we look into the problem of physical-layer secure transmission jointly with resource allocation in D2D communications. Existing research works for D2D underlay networks considered perfect channel state information (CSI), which is usually unrealistic in practical networks due to the wireless channels’ dynamic nature. Hence, unlike the previous works, we are considering imperfect CSI that include estimation errors. We leverage social-domain and frame a coalitional game approach, enabling multiple D2D pairs to share CU spectral resource. We assume heterogeneous cellular network based practical scenario considering multiple eavesdroppers, intra-cell interference, and inter-cell interference to evaluate achievable performances in terms of sum-rate and secrecy capacity for both CUs and D2D pairs. Moreover, we prove our proposed algorithm stability, convergence, and computational complexity. The simulation results establish the effectiveness of our proposed approach that not only maximizes the sum rate (i.e., 10 $ \%$ –60 $ \%$ ) but also improves the secrecy capacity (i.e., 20 $ \%$ –67 $ \%$ ) in comparison to the baseline schemes.

User Satisfaction-Aware Resource Allocation for D2D Enhanced Communication

Fog radio access networks has emerged as a promising evolution path for 5G network architecture to satisfy the explosively increasing demands of high-speed data services and massive access requirements of various devices. As a key complement to this path, proximity-based device-to-device (D2D) communications with high transmission rate can better support the above services. For resource allocation in D2D communications, there are two fundamental challenges that should be addressed: 1) how to utilize the mobile characteristics and social relationships among users to enhance the user throughput and 2) how to efficiently allocate the limited resources to guarantee each user’s service satisfaction. In this paper, a D2D dual-link enhanced communication model is developed to improve the throughput of D2D and cellular users (CUs) simultaneously. Then, based on the mobility characteristics and social relationships among users, an optimization problem about resource allocation is formulated and solved with the objective to maximize the user satisfaction. The simulation results verify that our proposed scheme can improve user satisfaction and throughput under limited spectrum resources simultaneously.

Energy Aware Resource Allocation in Multi-Hop Multimedia Routing via the Smart Edge Device

Environment sustainability, energy efficiency and computational reduction of computation time are recent stressing issues for the information and communication technologies (ICTs). Energy efficiency (EE) in particular is accounted for practical use of device to device (D2D) communications which is optimistic, and alternative architectural network especially for the cases where the infrastructure based communication is not possible. As a consequence, resource allocation for D2D communication plays a key role to address the issues related to battery lifetime and application quality of services (QoS). In this paper, a novel multi-hop routing is proposed to increase the energy efficiency of the D2D communication systems. In order to obtain a plausible solution, a combinatorial optimization is formulated. The purpose of this problem formulation is to achieve improved energy efficiency, throughput rate, resource utility, and packet delivery ratio as well as to reduce the computation cost. Network simulation demonstrates the efficiency of the proposed routing as compared to other existing techniques.

Interference Pricing based Resource Allocation for D2D Communications in Cellular Networks

Interference Pricing based Resource Allocation for D2D Communications in Cellular Networks

QoS-Aware Admission Control and Resource Allocation for D2D Communications Underlaying Cellular Networks

Device-to-Device (D2D) communications enable user equipments (UEs) in proximity to each other to exchange information by taking advantage of high data-rate and low energy consumption. When D2D transmissions share the radio resources with the cellular UEs, efficient admission control (AC) and radio resource allocation (RRA) strategies play a key-role in controlling the co-channel interference and allowing quality of service (QoS) provision to UEs. This paper proposes a novel joint AC and RRA strategy that provides long-term QoS support to cellular and D2D communications. The AC algorithm derives the best set of cellular and D2D links by maximizing the revenues of the service provider under QoS constraints. The RRA algorithm assigns the available channels and transmits powers to admitted users on the short-term, in order to maximize an average weighted sum-rate under the same QoS constraints of the AC. Due to the NP-hard nature of the optimization problem, we propose an AC greedy algorithm that achieves near-optimal results for reasonable numbers of D2D links. Then, we propose a low-complexity RRA algorithm that decouples channel and power allocation. Numerical results show that the proposed joint AC and RRA strategy outperforms existing frameworks by increasing up to 40% the number of satisfied cellular and D2D links and by reducing energy consumption by more than 50%.