Underlaying Cellular Networks Research Articles

Joint spectrum and power allocation scheme based on value decomposition networks in D2D communication networks

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity.

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.

Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure

Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.

Energy Efficiency-Oriented Joint Communication and Computation Resource Management in NOMA-Enabled V2X Underlaid Cellular Networks

Energy Efficiency-Oriented Joint Communication and Computation Resource Management in NOMA-Enabled V2X Underlaid Cellular Networks

Study on covert rate in the D2D networks with multiple non-colluding wardens

This paper investigates the covert communication of cellular link in the device-to-device (D2D)-enabled underlaying cellular network, including a base station, a cellular user, a D2D pair, and multiple non-colluding wardens. To conduct covert communication between the base station and the cellular user without being detected by the wardens, the underlaid D2D pair transmits with a random power to add uncertainty to wardens. We first model the detection of multiple non-colluding wardens, and the average minimum detection error probability of the worst case is derived. To measure the covert performance of the cellular link, the average covert rate of the cellular link and the outage probability of the D2D link are explored. Then, we formulate an optimization problem of maximizing the average covert rate under the constraint of the average minimum detection error probability, and an optimization algorithm is designed to solve the optimization problem to identify the optimal transmit power of the D2D pair. Last, the extensive numerical results are presented to show the impacts of network parameters on the covert performance, which indicates that although D2D communication causes interference to the cellular link, it can also help achieve covert communication by carefully designing its maximum transmit power.

Retracted: Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach

Retracted: Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach

Effectiveness of Guard Zone in Mitigating Interference in D2D Underlaid Cellular Networks

Device-to-device (D2D) communications, a promising technology, let cellular devices that are close to each other connect with one another directly, by passing the network infrastructure. As such, the technology provides the cellular network with many benefits, such as higher sum data rates, lower latency, extended coverage, higher energy efficiency, added security, higher spectral efficiency, and more useful services like inexpensive file caching. However, it comes with a cost—the interference underlaid D2D users inflict on cellular users at the base station when both use the same uplink frequency, which is the prefered reuse mode. This cost can, however, be reduced using a guard zone—a disk around the base station (BS) where D2D communications are not allowed. This simple and easy to implement approach, which has unfortunately not received enough research attention, is demonstrated in the present article to be highly effective in reducing the interference to cellular users. We leverage for this demonstration the mathematical tool of stochastic geometry. With the assumption that the BSs are distributed as a Poisson point process (PPP), we construct an analytical model to characterize the coverage probability of the cellular user in the presence of D2D nodes. The numerical results obtained, which are validated by rigorous Monte Carlo simulations, confirm that the guard zone approach is highly effective in protecting the cellular user from potential D2D interference.

Covert Rate Study for Full-Duplex D2D Communications Underlaid Cellular Networks

Device-to-device (D2D) communications underlaid cellular networks have emerged as a promising network architecture to provide extended coverage and high data rate for various Internet of Things (IoT) applications. However, because of the inherent openness and broadcasting nature of wireless communications, such networks face severe risks of data privacy disclosure. This paper investigates the covert communications in such networks for providing enhanced privacy protection. Specifically, this paper explores the critical covert rate performance in a full-duplex D2D communication underlaid cellular network consisting of a base station, a cellular user, a D2D pair with a transmitter and a full-duplex receiver, and a warden, where the D2D receiver can operate over either the full-duplex (FD) mode or the half-duplex (HD) mode. We first derive transmission outage probabilities of cellular and D2D links under the FD and HD modes, respectively. Based on these probabilities, we further provide theoretical modelling for the covert rate under each mode and explore the corresponding covert rate maximization by jointly optimizing the transmit powers of the D2D pair and the cellular user. To improve the covert rate performance, we propose a general mode in which the D2D receiver can flexibly switch between these two modes. Under the general mode, we also investigate the theoretical modelling and maximization problems of covert rate. Finally, we present extensive numerical results to illustrate the covert rate performances under the FD, HD, and general modes.

Sum Throughput Maximization Scheme for NOMA-Enabled D2D Groups Using Deep Reinforcement Learning in 5G and Beyond Networks

Device-to-Device (D2D) communication underlaying cellular network is a capable system for advancing the spectrum’s efficiency. However, in this condition, D2D generates cross-channel and co-channel interference for cellular and other D2D users, which creates an excessive technical challenge for allocating the spectrum. Despite this, massive connectivity is another issue in the 5G and beyond networks that need to be addressed. To overcome this problem, non-orthogonal multiple access (NOMA) is integrated with the D2D groups (DGs). In this paper, our target is to maximize the sum throughput of the overall network while maintaining the signal-to-interference noise ratio (SINR) of the cellular and D2D users. To achieve the target, a discriminated spectrum distribution framework dependent on multi-agent deep reinforcement learning (MADRL), termed a deep deterministic policy gradient (DDPG) is proposed. Here, it shares the global historical states, actions, and policies using the duration of central training. Furthermore, the proximal online policy scheme (POPS) is used to decrease the computation complexity of training. It utilized the clipping substitute technique for the modification and reduction of complexity at the training stage. The simulation results demonstrated that the proposed scheme POPS attains 16.67%, 24.98%, and 59.09% higher performance than the DDPG, Deep Dueling and deep Q-network (DQN).

Joint Resource Allocation Algorithm for Energy Harvest-Based D2D Communication Underlaying Cellular Networks Considering Fairness

In this paper, we aim to maximize the minimum data volume for the energy harvest (EH)-based D2D communication underlaying cellular network. The formulated problem is a mixed integer nonlinear programming (MINLP), which we split into two sub-problems to solve separately. We first employ the Successive Convex Approximation (SCA) method to solve the time and power allocation, while putting the spectrum multiplexed pairs as fixed. Then, we develop a fair allocation mechanism to allocate spectrum resources. Numerical results show that our developed algorithm outperforms other benchmark schemes in terms of the minimum and overall data volumes.

Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks

In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum of the average effective throughput of all cellular and D2D links in a cell accumulated over multiple time steps, where a cellular channel can be allocated to multiple D2D links. Allowing a cellular channel to be shared by multiple D2D links and considering performance over multiple time steps require a high level of system overhead and computational complexity so that optimal RA is practically infeasible in this scenario, especially when a large number of D2D links are involved. To mitigate the complexity, we propose a sub-optimal RA scheme based on a multi-agent DRL, which operates with shared information in participating devices, such as locations and allocated resources. Each agent corresponds to each D2D link and multiple agents perform learning in a staggered and cyclic manner. The proposed DRL-based RA scheme allocates resources to D2D devices promptly according to dynamically varying network set-ups, including device locations. The proposed sub-optimal RA scheme outperforms other schemes, where the performance gain becomes significant when the densities of devices in a cell are high.

Stochastic Game for Resource Management in Cellular Zero-Touch Deterministic Industrial M2M Networks

In this letter, we investigate the resource allocation problem for cellular Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) communications. We consider a scenario, in which multiple self-adaptive industrial M2M communications are deployed underlaying cellular networks to emulate a variety of vertical use cases in Industry 4.0 with the objective of guaranteeing the determinacy of latency. Specifically, in order to figure out the relationship between latency determinacy and the utilization of wireless resources meanwhile modeling the uncertainty of the stochastic environment, we firstly formulate the resource allocation problem as a stochastic game by jointly considering transmission power control, frequency spectrum allocation and the selection of base stations. Afterwards, a random graph-based sparse Long Short-Term Memory (LSTM) network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates the effectiveness of the proposed scheme.

Energy-efficient full-duplex D2D for SWIPT-empowered underlay cellular networks using a deep neural network

Close-packed small-cell networks enables a device-to-device (D2D) user equipment (DUE) to harvest energy from an ambient source, which increases the energy efficiency of the devices. Conceptually, full-duplex (FD) communications can double spectral efficiency compared to traditional half-duplex (HD) communications. However, the use of FD mode increases self-interference, which impacts spectral capacity and energy efficiency. In this paper, we study the performance of full-duplex D2D underlay cellular networks, where D2D devices decipher information and harvest energy simultaneously using SWIPT technology. Specifically, we build an optimization model to maximize the energy efficiency of the system, and obtain global- and sub-optimal solutions from the exhaustive search (ES) algorithm and the gradient search (GS) with barrier algorithm, respectively. In addition, we design deep neural networks (DNN) algorithm for the optimization model and evaluate the performance of the proposed DNN algorithm compared to the ES and GS algorithms. From the results, we confirm that the FD mode outperforms the HD mode in terms of energy efficiency and sum-rate and the proposed DNN algorithm can achieve near-global-optimal solution.

Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach

Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.

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.

Resource Allocation for Cellular Zero-Touch Deterministic Industrial M2M Networks: A Reinforcement Learning-Based Scheme

In this letter, we investigate the resource allocation problem for cellular zero-touch deterministic industrial machine-to-machine networks. We consider a scenario in which multiple self-adaptive industrial machine-to-machine (M2M) networks are deployed underlaying cellular networks to emulate a variety of vertical use cases in future Industry 4.0 with the objective of guaranteeing the determinacy of latency by learning-inspired resource allocation policy. Specifically, in order to figure out the relationship between latency determinacy and the utilization of wireless resources meanwhile modeling the uncertainty of the stochastic environment, we first formulate the resource allocation problem as a Markov game by jointly considering transmission power control, frequency spectrum allocation, and the selection of base stations. Afterward, a random graph-based sparse long short-term memory network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates the effectiveness of the proposed scheme.

Learning-Based Robust Resource Allocation for D2D Underlaying Cellular Network

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

Spectrum efficiency maximization for multi-hop D2D communication underlaying cellular networks: Machine learning-based methods

Multi-hop D2D communication has been proposed with the purpose of improving the coverage, quality of service (QoS), and flexibility and adaptability of single-hop D2D communication. However, multi-hop D2D communication often experiences obstacles caused by interference from the shared channel, which makes spectrum efficiency in multi-hop D2D networks an important issue to tackle. In this paper, we study the optimization of spectrum efficiency in multi-hop D2D communication underlaying cellular networks. First, we use iteration-based optimization techniques such as exhaustive search (ES) and gradient search (GS) with barrier function to find the global and local optimal solutions, respectively. More importantly, we propose two machine learning (ML) techniques, the unsupervised deep neural network (DNN) and deep Q-learning (DQL) algorithms and evaluate the performances of both algorithms compared to iteration-based optimization methods. The simulation results verify that both algorithms achieve near-global optimums compared to GS. Moreover, it is verified that the DQL outperforms the unsupervised DNN in terms of optimal spectrum efficiency, while the DQL algorithm has higher time complexity than the unsupervised DNN.

A Cooperative D2D Content Sharing Scheme Using NOMA Under Social Ties

With the merit of bypassing the base station (BS) to directly communicate between user equipment (UE), D2D content sharing between socially connected users has revealed promising potential in future networks. However, D2D receivers (DRs) in different D2D pairs (DPs) may also have demand for additional content sharing services from the D2D transmitters (DTs), who also have considerable social ties with them, in other surrounding DPs to enhance or supplement their received sharing services. In this letter, we propose a cooperative D2D content sharing scheme using NOMA technique under social ties, where the DRs in different DPs, who have social similarities in interested files, separately spare a portion of the transmit power of their corresponding DT to each other to improve the received sharing services in DRs. Based on this, the performance of the proposed scheme underlaying cellular network with partial channel state information (CSI) is analyzed, which is suitable for distributed deployment. Finally, the simulations validate the effectiveness of our proposed scheme.

Learning-based joint optimization of mode selection and transmit power control for D2D communication underlaid cellular networks

In device-to-device (D2D) communication, each transceiver pair can choose to operate in either direct D2D mode or device-infrastructure-device (DID) mode. This technique is called mode selection, which is important for establishing a reliable and efficient communication link that can significantly improve the network performance. However, the coexistence of direct D2D and DID communications in the same frequency band creates interference and degrades the performance of the system. Therefore, in this study, we aim to jointly optimize the mode selection and transmit power allocation of D2D pairs in D2D communication underlaid cellular networks. For this purpose, we first formulate a joint optimization model considering both mode selection and transmit power control of D2D pairs, which is an NP-hard combinatorial optimization problem with linear and nonlinear constraints. To solve this optimization problem, we design a deep neural network (DNN) structure. The proposed DNN algorithm is trained by minimizing the loss function which is obtained from Lagrange duality function. The weighting factor in the loss function is designed to decrease (increase) the rate of the receiver with strong (weak) interference compared to the mean channel gain. Simulation results show that the proposed DNN algorithm achieves a near-global optimal solution with lower computational complexity compared with the exhaustive search (ES) approach, and outperforms the solution obtained by using sub-optimal methods for Lagrange duality function.