Communication Underlaying Cellular Networks Research Articles

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.

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.

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.

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.

Outage Probability Analysis for Two-Way Amplify-and-Forward Mobile Relay Assisted Device-to-Device Communication in Rayleigh Fading Channel

Device-to-Device (D2D) communication underlaying cellular network is potential to enhance the spectral efficiency, reduce the energy consumption and improve individual quality-of-service (QoS) experience. But intra-cell orthogonality is impaired due to spectral reusing and severe co-channel interference could be imposed between cellular and D2D links. Besides, the direct links between local communicating pairs may be unfavorable and suffer an unsatisfied QoS experience since the randomness of channel condition. So, we are focused on analyzing the outage probability of D2D links assisted by two-way relays on Rayleigh fading channel. Specifically, we consider an amplify-and-forward relay protocol at relay due to their limited processing ability, and devise power control at both D2D transmitter and relay to maintain the QoS requirement of prioritized cellular links. The simulation results verify the correctness of the outage probability analyses for D2D links, and indicate the performance gain when relays are introduced to facilitate D2D links.

Coverage Probability Analysis for Device-to-Device Communication Underlaying Cellular Networks

As one of the most promising techniques in wireless communication systems, device-to-device (D2D) has drawn much attention due to its superior performance. Meanwhile, the interference between cellular users and D2D users is still a challenging problem and needs to be mitigated effectively. A large number of simulation experiments for D2D communications have been studied to reduce the impact of the interference in the existing literature. However, theoretical research is still lacking. Thus, in this paper, we use stochastic geometry to evaluate the uplink performance of users by considering the impact of the previous moment on the next moment, which captures the effect of temporal and the spatial correlation of the interference in D2D communication underlaying cellular networks. Using a Poisson point process to model locations of D2D users, we derive an analytic expression for conditional probability and unconditional probability of link success, and prove that the probability of link success is temporally correlated. Moreover, we provide a theoretical framework for interference mitigation in D2D underlaying cellular networks.

Sum-Rate Optimization for IRS-Aided D2D Communication Underlaying Cellular Networks

The intelligent reflective surface (IRS)-assisted communication is recognized as a promising technology to enhance capacity and coverage of the network by controlling propagation. However, in IRS-aided device-to-device (D2D) communication underlaying cellular networks, co-channel interference is more complicated and challenging than traditional D2D communication networks due to the addition of extra reflection paths. To alleviate interference and improve sum-rate, we establish a multivariable resource optimization function based on IRS-assisted D2D communication in this paper. Then, a hybrid sub-optimal algorithm is proposed, which includes channel allocation based on game theory, power allocation and phase shift optimization. Specifically, a sub-channel allocation algorithm is introduced to maximize system sum-rate, where the problem of sub-channel allocation is formulated as a two-stage combinatorial auction model, and the approximate optimal solution is obtained by bidding and adjustment. Then, a sub-optimal solution for transmission power and phase shifts is obtained through local search and iterative optimization. Simulation results show that the proposed hybrid sub-optimal algorithm is feasible and effective. Compared to the other 5 schemes, it has best sum-rate performance and lowest power consumption.

Optimal Interference for Device to Device Communication Underlaying Cellular Network

Device to Device (D2D) communication can effectively improve the spectrum eficiency and reduce the load of Base Station in cellular networks. However, when D2D users and cellular users share the wireless channels, it will bring signal interference for the cellular networks. In order to achieve mutual interference between users and base stations, and increase the spectrumutilization efficiency and energy utilization efficiency, this paper proposes a method control algorithm based on bearnforrning and interference alignment in D2D communication underlaying cellular networks. Simulation results show that the proposed algorithm can effectively reduce the interference of the BS transrnitting signal for D2D pais and significantly improve system capacity. Furthermore, D2D communication is more applicable to short-range links.

On selecting transmission mode for D2D transmitter in underlay cellular network with a multi-antenna base station

The Device-to-Device (D2D) communication underlaying cellular networks is considered in this study. The D2D transmitter in the D2D mode can directly transmit messages to a receiver, but it may interfere with the transmission of another cellular user who shares the same uplink channel. The transmitter can also operate in a cellular mode in which no interference to another cellular user occurs. We propose a mode selection scheme that aims to minimize the transmission power of the D2D transmitter subject to constraints on the minimum required data rate and maximum interference to other cellular users. The proposed scheme is based on bounds for transmission power and is less complex than the optimal scheme. Furthermore, it requires only a few statistics and does not need a fading channel distribution. The performance of the scheme is close to optimum when the number of Base Station (BS) antennas is large, and the mean absolute deviation of the fading terms is small. We verify this with numerical results of the Rician and Rayleigh fading channels by assuming that the BS antennas are independent. The simulation results for the two correlated BS antennas are presented herein.

Deep Reinforcement Learning-Based Dynamic Spectrum Access for D2D Communication Underlay Cellular Networks

This letter investigates a deep reinforcement learning (DRL)-based spectrum access scheme for device-to-device (D2D) communication underlay cellular networks. Specifically, cellular users (CUEs) and D2D pairs attempt to access the time slots (TSs) of a shared spectrum, and TSs are dynamically scheduled to CUEs in different frames. Based on the DRL theory, D2D pairs can be seen as a centralized agent which aims to learn an optimal spectrum access strategy to maximize the sum throughput without any prior information. In particular, with different locations of CUEs, the spectrum access manners for D2D communication are changed to ensure the communication quality of CUEs at the cell edge. Then, a double deep Q-network (DDQN) based D2D spectrum access (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> SA) algorithm is proposed, which makes D2D pairs learn to decide whether to access the spectrum in different TSs. Moreover, to ensure the fairness of resource allocation among D2D pairs, we improve the proposed algorithm and incorporate fairness into the objective function. Simulation results show that our proposed algorithm can achieve an optimal sum throughput close to the theoretical upper bound, where the performance is significantly improved compared to the scheme based on base station cooperation.

Energy-Efficient Transmission Rate Selection and Power Control for Relay-Assisted Device-to-Device Communications Underlaying Cellular Networks

Device-to-device (D2D) communication underlaying cellular networks is seen as a promising technology for future communication systems, especially when relays are employed to extend the range of D2D links. However, the mutual interference between the spectrum-sharing D2D links and conventional cellular (CC) links is still the dominating performance limiting factor, which enforces those links to adopt higher transmission power. As energy saving is always one of the key concerns, we propose to minimize the total transmission energy consumption of a relay-assisted D2D link and a CC link sharing radio resources by jointly optimizing their transmission rate selection and power control (TRSPC). We formulate the optimization problem as a non-convex non-linear programming (NLP) problem and then develop a game theory based distributed approach to solve it. Simulation results demonstrate that our game theory based TRSPC approach can enormously decrease the total transmission energy consumption compared with existing works.

Energy Efficiency Maximization of Two-Time-Slot and Three-Time-Slot Two-Way Relay-Assisted Device-to-Device Underlaying Cellular Networks

The continuous development of fifth generation (5G) communication and Internet of Thing (IoT) inevitably necessitates more advanced systems that can satisfy the growing wireless data rate demand of future equipment. Device-to-Device (D2D) communication, whose performance is evaluated in terms of the overall throughput, energy efficiency (EE) and spectral efficiency (SE), is considered a promising solution for the aforementioned problem. Thereby, this paper aims at improving the performance of the D2D communication underlaying cellular networks operating on multiple bands by maximizing the EE in its uplink. Thanks to the stochastic geometry theory, it is possible to derive the closed-form expressions for the successful transmission probability (STP), the total average transmission rate (TATR), and the total average energy efficiency (TAEE) of cellular and D2D users in different time slot setting. Particularly investigated and compared in this study, there are one-hop, direct, D2D communication in two time slots (2TS), and multi-hop, indirect, D2D communication in three time slots (3TS) with an additional D2D user acting as a two-way relay to assist the communication. Moreover, an optimization problem is formulated to calculate the maximum TAEE of D2D users and the optimum transmission power of both the cellular and D2D users. Herein this optimization study, which is proven to be non-convex, the Quality of Service (QoS) is ensured as the STP on every link is considered. The herein approach is referred to as relay-assisted D2D communication which is capable of delivering a notably better QoS and lower transmission power for communication among distant D2D users.

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.

A Two-Layer, Energy-Efficient Approach for Joint Power Control and Uplink-Downlink Channel Allocation in D2D Communication.

Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink–downlink resources and guaranteeing the cellular users’ (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn–Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.

Power allocation and resource assignment for secure D2D communication underlaying cellular networks: A Tabu search approach

The primary goal of introducing device-to-device (D2D) communication underlaying cellular systems is to increase spectral efficiency (ES). However, it can lead to performance degradation caused by severe interference between cellular and D2D links. In addition, due to the complexity of control and management, direct connections between nearby devices are vulnerable. Thus, D2D communication is not robust against security threats and eavesdropping. However, if the physical layer security (PLS) concept is taken into account, the interference can help reduce eavesdropping. In this work, we address power control and resource block (RB) assignment problem to improve security of D2D communication and cellular network while satisfying the minimum rate requirements and power budget of the users. To solve this problem, we employ a decomposition method and individually address the power allocation and the RB assignment problem with lower complexity. However, RB assignment problem reduces to the three-dimensional matching problem, which is under NP-hard category and an optimal solution can be obtained through a complicated method such as Brute-force search or branch-and-bound with exponential time complexity. We, therefore, propose an approach based on tabu search (TS) meta-heuristic algorithm with reduced time complexity to globally find the near-optimal RB assignment solution. Moreover, we evaluate the performance of proposed scheme with a genetic algorithm (GA) based approach and other baseline methods. Simulation results show that the applied TS algorithm outperforms the GA since it employs both exploitation and exploration through a memory-based local search method and a perturbation mechanism, respectively. However, the GA, as a population-based method, focuses only on exploration by searching the entire search space without concentration on the current solution.

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

Device-to-device (D2D) communication underlay cellular networks is a promising technique to improve spectrum efficiency. In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which imposes a great technical challenge to spectrum allocation. Existing centralized schemes require global information, which causes a large signaling overhead. While existing distributed schemes requires frequent information exchange among D2D users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named multi-agent actor critic (MAAC). MAAC shares global historical states, actions and policies during centralized training, requires no signal interaction during execution and utilizes cooperation among users to further optimize system performance. Moreover, in order to decrease the computing complexity of the training, we further propose the neighbor-agent actor critic (NAAC) based on the neighbor users’ historical information for centralized training. The simulation results show that the proposed MAAC and NAAC can effectively reduce the outage probability of cellular links, greatly improve the sum rate of D2D links and converge quickly.

Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network

For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system, there is a limit to allocating resources considering a complicated interference between different links in a centralized manner. If D2D link is controlled by an enhanced node base station (eNB), and thus, remains a burden on the eNB and it causes delayed latency. This paper proposes a fully autonomous power allocation method for IoT-D2D communication underlaying cellular networks using deep learning. In the proposed scheme, an IoT-D2D transmitter decides the transmit power independently from an eNB and other IoT-D2D devices. In addition, the power set can be nearly optimized by deep learning with distributed manner to achieve higher cell throughput. We present a distributed deep learning architecture in which the devices are trained as a group but operate independently. The deep learning can attain near optimal cell throughput while suppressing interference to eNB.