Underlay D2D Communication Research Articles

Joint mode selection and resource allocation for underlaying D2D communications: matching theory

Joint mode selection and resource allocation for underlaying D2D communications: matching theory

Jointly Optimal RIS Placement and Power Allocation for Underlay D2D Communications: An Outage Probability Minimization Approach

Jointly Optimal RIS Placement and Power Allocation for Underlay D2D Communications: An Outage Probability Minimization Approach

Power Allocation for NOMA With Cache-Aided D2D Communication

Communication networks are becoming increasingly content-centric, and reusable content like viral information and video on demand are often requested by multiple users. Caching at the user equipment enables device-to-device (D2D) communications to be used to deliver requested contents which will help reduce data traffic at base stations and also enhance the achievable data rates. In this paper, we propose a system whereby users are able to exchange valuable cached content with each other via a D2D link which underlays the reception of a downlink non-orthogonal multiple access (NOMA) signal. We formulated a sum rate maximization problem that is subject to minimum rate constraints and derived optimal solutions depending on which user is the D2D transmitter. Additionally, sub-optimal solutions based on a negligible self-interference assumption are also proposed. Simulation results demonstrate the significant performance gains of underlaid D2D communications as compared with optimal downlink cellular NOMA. Furthermore, results on the self-interference (SI) cancellation factor highlight that the sub-optimal power allocation solution offers sum rate performance close to the optimal case. The performance gains are further enhanced when SI cancellation is high.

Power Allocation-Based NOMA and Underlay D2D Communication for Public Safety Users in the 5G Cellular Network

Cellular networks (CNs) continue to evolve by implementing several technologies such as nonorthogonal multiple access and device-to-device communication, accommodating thereby the high data traffic demand. By leveraging these technologies, in this article, we propose a novel scheme to integrate public safety (PS) networks in CNs. This integration enhances the performance of PS users (PSUs) that rely on low-rate technologies. We formulate a mixed integer nonlinear programming problem for sum throughput maximization, to optimize the channel allocation and achieve the required rate of PSUs. This optimization takes into account power budget, users required rates, and successful interference cancellation constraints. Since the maximization problem is computationally challenging, we design a heuristic algorithm that selects the appropriate cellular users to share their resources with PS clusters. Then, given this selection, we compute the optimal power allocation in each PS cluster using the Lagrange multiplier method. Evaluation results demonstrate that our approach improves spectral efficiency and provides higher sum throughput compared to other works in the literature. We also conduct extensive simulations to compare our throughput maximization approach with the fairness maximization approach. Finally, we compute the outage probability to reassign PSUs groups to new resource blocks if necessary.

Joint throughput and coverage maximization for moving users by optimum UAV positioning in the presence of underlaid D2D communications

A fundamental challenge in using unmanned aerial vehicles as a flight base station is establishing coverage and creating the best possible rate. Drone location determines the maximum coverage probability and available rate. The paper considers a moving UAV as an aerial base station for downlink users with Gauss–Markov random movement and receiving interference from two users. First, we consider several points randomly in the area covered by the drone to find the optimal position. Using Algorithm (1), we calculate the maximum radius that the UAV can cover. After that, the algorithm (2) determines the distance of users to random points, the probability of coverage, and the available rate. In the end, we calculate the optimal point of the drone based on Algorithm (3). The results of the simulation using MATLAB show that after finding the optimal position of the drone, 95 percent of the users are covered and 0.15 Gbps is the average available rate for downlink users. Compared to other methods, it has the highest value.

Robust Distributed Power Control with Resource Allocation in D2D Communication Network for 5G-IoT Communication System

In the domain of communication technologies, the Device-to-Device (D2D) communication becomes a predominant technology for the implementation of 5G communication system and Internet of Things (IoT) applications. In D2D communication Network, resource allocation and power management are the key areas of interest with ensuring the Quality of Services (QoS). Firstly, we propose the power control problem which is a non-convex problem. By using the log transformation approach, the non-convex problem converts into the convex optimization problem. Robust distributed power control method is further utilized for the power optimization at both ends (base station and D2D user) for underlay Inband D2D communication, where the cellular user and D2D user both use the cellular spectrum. After the power control, resource allocation is done to maximize the energy efficiency by 66.67% for the D2D system. Our proposed work provides new insight to power control techniques in D2D communication. Numerical analysis of the proposed algorithm reflects the impact of robust distributed power control for maintaining the quality of services and enhancing the energy efficiency of the system.

Optimizing the operator’s economy in multi-tier cellular networks with underlaid D2D communications

In this paper, we consider a three-tier cellular network, consisting of a Macrocell base station, several Femtocell base stations, cellular users, and Device-to-Device (D2D) users. Each user is attached to the Macrocell or one of the Femtocells while D2D users communicate directly. The Macrocell divides the spectrum into several subchannels. The Femtocell users are allowed to communicate over the common spectrum which is allocated to one of the Macrocell users. We consider the scenario in which the Femtocells do not interfere each other while the Macrocell users experience interference from Femtocells and vice versa. Likewise, the Macrocell users would suffer from the interferences caused by underlaid Femtocells and D2D communications in the same spectrum. The average outage probability over the shared sub-channel is investigated assuming the effect of D2D users, and the closed form expressions are obtained in an interference limited scheme. Then, the object function is defined to minimize the operator’s cost function for establishing Femtocell network. The problem is generally non-convex and hence, it is solved through separation to subproblems to derive the near-optimal solutions for the intensity of Femtocells and power allocation. The analytical results are verified by the simulation results, which show that although the D2D and Femtocells improve the network performance by handling the data traffic, increasing the D2D users’ transmission power and the intensity of physical distribution of Femtocells increase the network cost function.

Energy Efficient Underlaid D2D Communication for 5G Applications

With the increasing importance of reduced-energy wireless communication in 5G networks, maximizing the energy efficiency of device-to-device (D2D) communication has gained a lot of research interest. But so far, the methods have only been able to address the single-cell scenario. This paper proposes an algorithm to improve the non-convex problem in underlaid D2D communication for multiple band scenarios. In 5G wireless communications, device to device is one of the methods used for achieving better energy efficiency. It also reduces the throughput latency. The optimization problem is formulated with a derivative algorithm and proposed modified derivative algorithm. Both the algorithms are compared, and this comparison shows that the modified derivative algorithm is more efficient than the derivative algorithm.

A matching-theoretic approach to resource allocation in D2D-enabled downlink NOMA cellular networks

This paper investigates the resource allocation problem in non-orthogonal multiple-access (NOMA) cellular networks underlaid with OMA-based device-to-device (D2D) communication. This network architecture enjoys the intrinsic features of NOMA and D2D communications; namely, spectral efficiency, massive connectivity, and low-latency. Despite these indispensable features, the combination of NOMA and D2D communications exacerbates the resource allocation problem in cellular networks due to the tight coupling among their constraints and conflict over access to shared resources. The aim of our work is to maximize the downlink network sum-rate, while meeting the minimum rate requirements of the cellular tier and underlay D2D communication, and incorporating interference management as well as other practical constraints. To this end, many-to-many matching and difference-of-convex programming are employed to develop a holistic sub-channels and power allocation algorithmic solution. In addition to analyzing the properties of the proposed solution, its performance is benchmarked against an existing solution and the traditional OMA-based algorithm. The proposed solution demonstrates superiority in terms of network sum-rate, users’ connectivity, minimum rate satisfaction, fairness, and interference management, while maintaining acceptable computational complexity.

Modified of Mixed Strategy Algorithm for Power Allocation on D2D Underlay Communication

As time passes by, the need for telecommunication services will increase along with increasement of human’s population. To anticipate the flooding requests of telecommunication services, one of the solutions offered is by implementing D2D communication, especially in 5G architecture. In D2D 5G, focusing on underlay communication D2D uses the same frequency spectrum, so that opportunity of interference may happen, but the value of spectral efficiency (SE) may increase as a result of the effectiveness of the spectral that occurs. In terms of financing and its development, underlay D2D communication is considered more profitable for service providers. Based on this point, the authors propose a novel algorithm of modified mixed strategy algorithm for power allocation to make the power transmission cost be more effective. After the experiment conducted, the result shows that modified mixed strategy outperform baseline model which uses no power allocation algorithm.

Resource Allocation for IRS-Aided JP-CoMP Downlink Cellular Networks With Underlaying D2D Communications

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, an IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter (DT). The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service (QoS) requirement of CUs, the power allocations for both CUs and D2D pairs, and the limited backhaul capacity. To handle the non-convex optimization problem, we propose a computationally efficient iterative algorithm exploiting the big-M formulation, the penalty method, and the successive convex approximation, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Simulation results demonstrate that the proposed scheme can increase the system sum-rate by 70% and 20% compared with the schemes with no IRS and random phase shifts, respectively, when the minimum required SINR of CUs is 5 dB. Additionally, our results confirm the superiority of introducing IRS for harnessing interference in wireless communication systems.

Relay Selection for Improving Physical Layer Security in D2D Underlay Communications

This paper investigates the physical layer security of inband underlay Device-to-Device (D2D) communication, where the direct link between D2D users is not available. In this respect, optimal relay and suboptimal relay selections are utilized to secure the D2D transmission. The eavesdropper uses either maximal-ratio combining or selection combining to increase the wiretapped signals. The D2D secrecy performance analysis is performed regarding the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity, and closed-form expressions are provided for both relay selection approaches and verified using Monte-Carlo simulation. From the numerical results, it is shown that increasing the number of D2D relays enhances the secrecy performance of D2D communications. Moreover, the exact asymptotic analysis of the SOP is provided. It turns out that the diversity order of both relay selection approaches is the same.

Lyapunov optimization machine learning resource allocation approach for uplink underlaid D2D communication in 5G networks

Device-to-device (D2D) communication plays a vital role in communication technologies with resource management and power control, which are the major issues for researchers in the era of 2020. In 5G networks, machine learning algorithms play a crucial role to manage these issues. In the proposed work, the problem is formulated for the uplink underlaid model of D2D communication. The Lyapunov optimization technique is utilized with a combination of machine learning techniques for resource allocation in D2D communication. First, the maximization of uplink and overall system capacity is formulated with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed integer non-linear problem which uses the Lyapunov optimization method to optimize the bit error rate value and iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, the support vector machine technique is utilized to ensure the spectral efficiency of an overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies.

Trajectory planning in UAV emergency networks with potential underlaying D2D communication based on K-means

At present, unmanned aerial vehicles (UAVs) have been widely used in communication systems, and the fifth-generation wireless system (5G) has further promoted the vigorous development of them. The trajectory planning of UAV is an important factor that affects the timeliness and completion of missions, especially in scenarios such as emergency communications and post-disaster rescue. In this paper, we consider an emergency communication network where a UAV aims to achieve complete coverage of potential underlaying device-to-device (D2D) users. Trajectory planning issues are grouped into clustering and supplementary phases for optimization. Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed, respectively. In addition, in order to balance sum throughput with trajectory length, we present a joint evaluation index. Then relying on this index, a third trajectory optimization algorithm is further proposed. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

Resource Allocation for Downlink Full-Duplex Cooperative NOMA-Based Cellular System with Imperfect SI Cancellation and Underlaying D2D Communications.

In this paper, the interplay between non-orthogonal multiple access (NOMA), device-to-device (D2D) communication, full-duplex (FD) technology, and cooperation networks is proposed, and a resource allocation problem is investigated. Specifically, a downlink FD cooperative NOMA-based cellular system with underlaying D2D communications is proposed, where, in each NOMA group, the strong user assists the weak user as an FD relay with imperfect self interference (SI) cancellation. In terms of reaping spectral efficiency benefits, the system sum rate is to be maximized by optimizing channel allocation. This optimization is based on quality of service (QoS) constraints of D2D pairs and cellular users (CUs), power budget of base station and strong user (cooperative phase), and successive interference cancellation (SIC) constraints. Since the maximization formulated problem is computationally challenging to be addressed, a two-sided stable many-to-one matching algorithm, based on Pareto improvement, performs sub-channel assignment. Extensive simulations are implemented to demonstrate the system performance indicated by different metrics.

Pilot Allocation and Power Optimization of Massive MIMO Cellular Networks With Underlaid D2D Communications

Pilot pollution and limited power have become two important factors limiting the throughput of massive multi-input–multioutput (MIMO) systems. To improve system performance, we consider device-to-device (D2D) communication underlay massive MIMO (denoted “massive MIMO-D2D” for short) cellular networks and then perform pilot allocation and power optimization under this network. To begin with, the closed-form spectrum efficiency (SE) expressions for different types of users are derived in the massive MIMO-D2D cellular network. Then, we analyze the deficiencies of the existing pilot allocation schemes and propose a new pilot allocation problem, i.e., the SE product is maximized for enhancing the system SE and ensuring fairness among the users simultaneously. To solve the maximum SE product problem, we develop a pilot gray wolf prey (PGWO) algorithm by designing the fitness value used to measure pilot pollution and the global objective function used to evaluate the quality of pilot allocation. The PGWO algorithm can find the optimal SE accurately through a global search, and it is suitable for the pilot allocation of different models from single cell to multicell. Besides, we formulate the maximum–minimum fairness problem for power optimization and prove that the power objective function conforms to linear programming, and a bisection algorithm is provided to optimize the power. Simulation results show that the proposed SE product problem with the proposed PGWO algorithm promotes fairness for users while further enhancing SE compared to the existing pilot allocation schemes, and joint pilot allocation and power optimization achieves great sum SE over only pilot allocation.

An Efficient Channel and Power Allocation Scheme for D2D Enabled Cellular Communication System: An IoT Application

The Device-to-device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. Resource management is an important aspect for realization of D2D communication in upcoming IoT networks. In this article, we select underlay in-band D2D communication as it is more beneficial in terms of spectral efficiency even though it comes at the cost of interference with cellular communication. To deal with this difficulty, we propose a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D2D pair reuses multiple channels from different cellular users (CUs). The proposed scheme adopts a channel selection technique, wherein multiple channels of CU can be shared by each D2D user. Furthermore, optimal power for each D2D user is determined through the Lagrangian dual optimization method. The formulated power control maximization scheme nicely balances the total transmission power of D2D and D2D sum-rate. The proposed channel and power allocation problem aims at maximizing the D2D sum-rate by increasing the number of active D2D links while preserving the quality of service (QoS) of CU. Finally, a relationship between energy efficiency (EE) and transmit power of D2D is investigated through an EE maximization problem. The overall system performance is evaluated in terms of the D2D shared ratio, throughput gain of the network, and computational complexity of the proposed optimal strategy. Further, the merits of using the proposed resource sharing scheme over the existing schemes are also verified through numerical results.

Energy efficiency maximisation based on RALA protocol for underlay D2D communication

Energy efficiency (EE) maximisation is one of the most important necessities of the fifth-generation (5G) wireless networks. In this study, both power allocation and channel assignment issues for the cellular network underlaid with device to device (D2D) communication are introduced. The main objective is to assign the users’ resources in order to maximise the total EE of the network. The distributions of both the D2D users (DUs) and the cellular users (CUs) are based on the stochastic geometry theory. Hence, the mathematical formula for the optimisation problem is demonstrated. Additionally, the resource allocation link admission (RALA) algorithm is proposed to achieve the maximum EE. Moreover, it assigns the optimal powers for each CU and DU in the system. Furthermore, the best channel assignment for each new D2D link request is determined. The optimisation problem in the RALA scheme is solved by utilising a proposed modified Cuckoo search algorithm. Furthermore, the complexity analysis for the proposed scheme is introduced. From simulation results, the proposed RALA scheme is efficient to assign the resource allocation for the users to achieve the near-optimal EE.

Reconfigurable Intelligent Surface Assisted D2D Underlay Communications: A Two-Timescale Optimization Design

The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, a reconfigurable intelligent surface (RIS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead, where transmit beamforming at the base station (BS) and power control at the D2D transmitters are adapted to instantaneous effective channel state information (CSI); and the RIS phase shifts are adapted to slow-varying channel mean. Based on the two-timescale optimization scheme, we aim to maximize the D2D ergodic weighted sum-rate (WSR) subject to a given outage probability constrained signal-to-interference-plus-noise ratio (SINR) target for each CU. The two-timescale problem is decoupled into two sub-problems, and the two sub-problems are solved iteratively. Numerical results verified that the two-timescale based optimization performs better than two baselines, and also demonstrated a favourable trade-off between system performance and CSI overhead.

Power Optimization in Device-to-Device Communications: A Deep Reinforcement Learning Approach With Dynamic Reward

Device-to-Device (D2D) communication can be used to improve system capacity and energy efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is to extend network lifetime by efficient and effective resource management. Deep reinforcement learning (RL) provides a promising solution for resource management in wireless communication systems. This letter aims to maximise the EE while satisfying the system throughput constraints as well as the quality of service (QoS) requirements of D2D pairs and cellular users in an underlay D2D communication network. To achieve this, a deep RL based dynamic power optimization algorithm with dynamic rewards is proposed. Moreover, a novel algorithm with two parallel deep Q networks (DQNs) is designed to maximize the EE of the considered network. The proposed deep RL based power optimization method with dynamic rewards achieves higher EE while satisfying the system throughput requirements.