D2D Links Research Articles

Resource allocation for device-to-device communications underlaying uplink cellular networks

Underlying cellular networks, device-to-device (D2D) communications are a practical network technology that can increase power efficiency and spectrum usage for close-proximity wireless services and applications. However, D2D link interference, when sharing resources with cellular users (CUs), poses a major challenge in such distribution situations. In this research, we primarily utilize wireless channel data that exhibits slowly shifting large-scale fading to conduct spectrum sharing and power allocation. The overall ergodic capacity of all cellular user equipment (CUE) links is initially considered as the optimization target in order to maximize the overall throughput of CUE links while ensuring the reliability of each D2D link. Then, the expansion of the minimum ergodic capacity is measured to ensure a more consistent capacity performance across all CUE links. We utilized algorithms that are resilient to channel fluctuations and produce optimal resource allocation. We use MATLAB, and the computer simulation validates its intended performance.

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.

RIS-Assisted D2D Communication over Nakagami-m Fading with RSMA.

In this study, we investigated reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems over Nakagami-m fading channels. To enhance the reliability of RIS-assisted D2D communications, we utilized the rate-splitting multiple access (RSMA) technique to maximize the achievable ergodic rate for our considered systems. Specifically, both devices decoded the common symbol by treating private symbols as interference, and then each private symbol was decoded by treating the other as interference. In order to maximize the achievable ergodic rate at the destination, we analyzed the achievable ergodic rate of the RIS link and the D2D link, and the destination jointly decoded both symbols transmitted from the source and device by involving the maximum ratio combination (MRC). We obtained a closed-form expression for the achievable ergodic rate of the proposed RIS-assisted D2D communication system. Finally, we investigated the influence of power allocation factors and the number of reflective elements on the achievable ergodic rate. As seen by the numerical results, there was a good match between the analysis and simulation results, as well as significant superiority compared with existing works.

D2D Self Organization in IOT via Triple Modular Redundancy Based MDS Code

Device-to-Device (D2D) communication is a key enabling technology to replace base station-to-device(B2D) communication. D2D with IoT realization has spectral and energy-efficient features for on-time quality data transfer. However, the rapid growth of such networks and the dynamic quality of service demands of user equipment require robust and efficient service quality in disasters, outages, and adversarial conditions. To address this challenge, this paper proposes a triple modular redundancy based on the maximum distance separable (TMR-MDS) technique has been proposed, which modifies the repair bandwidth by utilizing more storage occupation and improving the D2D link availability in the system. The proposed TMR-MDS technique will be used, which is composed of two phases: trust management modules and triple modular redundancy storage modules. As part of the initial stage of trust management, a fuzzy logic model is employed to determine the user’s trust value after the user’s behaviour is examined. Second, using the majority voting scheme to improve triple modular redundancy storage. Furthermore, if any problem occurs in the node, then it will self-organize. MATLAB is used to evaluate the performance of the proposed system. The overall system performance is evaluated in terms of the network’s trust level, and the communication cost of the proposed strategy. As per the experimental results, the proposed TMR-MDS method has a better performance of 12.67%, 23.68%, and 25.87% than hierarchical bi-partite, double replication MDS, and unequal repairing locality code.

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.

Channel Modeling and Characterization of Access, D2D and Backhaul Links in a Corridor Environment at 300 GHz

Channel Modeling and Characterization of Access, D2D and Backhaul Links in a Corridor Environment at 300 GHz

Joint Power Control and Resource Allocation for Optimizing the D2D User Performance of Full-Duplex D2D Underlying Cellular Networks

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D’s short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system’s SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn–Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn–Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effectively enhances the performance of DUs in an FD-D2D underlying cellular network when compared to the sum rate maximization design.

Joint User-Side Recommendation and D2D-Assisted Offloading for Cache-Enabled Cellular Networks With Mobility Consideration

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

Resource Allocation for RIS-Assisted Device-to-Device Communications in Heterogeneous Cellular Networks

In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency band. In the general environment, Heterogeneous Cellular Networks (HCNs) including millimeter wave (mm-wave) have appeared. Since the D2D link allows to share of spectrum resources with the cellular user, it will bring potential interference to the cellular user. Fortunately, an emerging technology called Reconfigurable Intelligent Surface (RIS) can mitigate the severe interference caused by D2D links by shaping the incident beam and improving the multipath phase shift. In this paper, we study the resource allocation scheme to maximize the system sum rate, in the RIS-assisted single-cell heterogeneous D2D communication scenario. To solve the Block Coordinate Descent (BCD) problem, the problem of maximizing the sum rate is decomposed into three sub-problems. The resource allocation sub-problem is solved by a coalitional game method based on the game theory. The power allocation problem of the coalition converts the concave function into a convex optimization by mathematical transformation. The problem is solved by the gradient descent method. The local search method is adopted to find the optimum for the phase conversion problem. Then iterate until the difference of sum rate is less than the threshold. The simulation results show that the designed algorithm is superior to other benchmark schemes in the literature.

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.

On Power-Efficient Low-Complexity Adaptation for D2D Resource Allocation with Interference Cancelation.

This paper presents a detailed framework for adaptive low-complexity and power-efficient resource allocation in decentralized device-to-device (D2D) networks. The adopted system model considers that active devices can directly communicate via specified signaling channels. Each D2D receiver attempts to allocate its D2D resources by selecting a D2D transmitter and one of its spectral channels that can meet its performance target. The process is performed adaptively over successive packet durations with the objective of limiting the transmit power on D2D links while reducing the processing complexity. The proposed D2D link adaptation scheme is modeled and analyzed under generalized channel conditions. It considers the random impact of potential D2D transmitters as well as the random number of co-channel interference sources on each D2D link. Interference cancelation schemes are also addressed to alleviate co-channel interference, which can ease the D2D resource allocation process. Generalized formulations for the statistics of the resulting signal-to-interference plus noise ratio (SINR) of the proposed adaptation scheme are presented. Moreover, generic analytical results were developed for some important performance measures as well as processing load measures. They facilitate tradeoff studies between the achieved performance and the processing complexity of the proposed scheme. Insightful results for the distributions of SINRs on individual D2D links under specific fading models are shown in this paper. The results herein add enhancements to some previous contributions and can handle various practical constraints.

To Continue Transmission or to Explore Relays: Millimeter Wave D2D Communication in Presence of Dynamic Obstacles

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses. Dynamic obstacles may cause unpredictable fluctuations to D2D channel quality and hence a D2D relay initially chosen by base station (BS) might undergo failed transmissions resulting in severe packet loss and delay. This local information regarding link quality deterioration needs to be informed to the BS by the user equipments (UEs) which may result in some delay. Also, exploring a new relay on mmWave channel results in significant delay due to directional search. Hence the following optimal sequential decision must be made when packet loss occurs: whether to explore for a new relay link considering exploration cost, or to continue communication via the existing relay. We model this sequential decision problem locally at each UE as partially observable Markov decision process to capture uncertainty in D2D links while minimizing delay. We derive an optimal threshold policy for both positively and negatively correlated links. We also provide a simplified and easy to implement policy based on successive acknowledgment failures for positively correlated links. Through simulation, we validate theoretical findings and demonstrate that our approach outperforms existing state of the art algorithms.

Distributed Double Auction Mechanisms for Large-Scale Device-to-Device Resource Trading

While some mobile users in wireless networks may experience temporal scarcity of wireless network resources such as data plan, computation capacity and energy storage, some others may leave them underutilized. If the appropriate market existed, users connected locally with D2D links could exchange such resources with low communication cost and realize significant efficiency gains by reducing waste and achieving resource pooling. This paper proposes such a D2D trading market that scales for large numbers of users. Contrary to traditional resource allocation solutions that are mostly centralized, our double auction mechanism exploits local D2D connectivity and uses distributed computation to achieve near-optimal allocative efficiency. The final prices for each matched pair of buyer and seller are adjusted in a way to induce incentive compatibility and depend on their own declarations in terms of quantity and valuation. We prove that the overall mechanism has significant social welfare gains compared to other widely-used distributed pricing mechanisms. It is also individually rational, ex-ante budget balanced using a subscription fee, and robust to perturbations of the model parameters. To render the system fully manipulation-proof, we further propose a distributed auditing scheme that prevents users from altering the decentralized computation to increase their profits. Finally, we model the repeated execution of the mechanism and determine the best trading frequency by taking into account the arrivals and departures of new participants.

RIS-assisted edge-D2D cooperative edge computing for industrial applications

Intelligence and low latency are particularly desired in the vision of industrial intelligence. To support intelligence, massive amount of data is generated from distributed Internet of Things (IoT) devices, and expected to quickly process with artificial intelligence (AI) for data value maximization. To reduce the network transmission delay from data source to computing nodes, edge computing is promising. However, the scarce and dynamic wireless resource as well as limited computing capability of edge servers challenge the edge intelligence. This paper studies a reconfigurable intelligent surface (RIS)-assisted edge-device-to-device (D2D) cooperative edge computing for end-to-end delay reduction in industrial environments. In special, we consider such an edge-D2D cooperative edge computing system, where the IoT devices could offload their tasks to edge server via cellular links for edge computing, or transmit the tasks to helper nodes via D2D links for local computing. We also consider the base station (BS)-based and D2D-based RISs deployed in cellular and D2D networks respectively. We formulate the joint computation offloading, beamforming optimization of both BS-based and D2D based RISs, and CPU resource allocation problem for average end-to-end delay minimization. Then, a distributed and cooperative scheme, called RIS-assisted edge-D2D cooperative computation offloading (RIS-assisted EDCO), is proposed to address the problem. The simulation results have illustrated the efficiency of the proposal for low end-to-end delay performance provisioning.

Analysis and Optimization of Spectral and Energy Efficiency in Underlaid D2D Multi-Cell Massive MISO Over Rician Fading

This paper investigates the uplink spectral efficiency (SE) characterization and energy efficiency (EE) optimization of device-to-device (D2D) communications underlying a multi-cell massive multiple-input single-output (m-MISO) network, assuming that the channels are modeled with Rician fading. First, an analytical expression for the lower-bound of the ergodic capacity of a typical cellular user (CU) is derived with imperfect channel state information in the presence of D2D links’ interference. Then, a closed-form approximate achievable SE expression for a typical D2D pair is derived considering Rician fading between D2D pairs. The asymptotic SE behavior is analyzed in strong line-of-sight (LOS) conditions for CU and D2D pairs. Also, simplified expressions are obtained for the special case of Rayleigh fading. Next, to optimize the total EE, a transmit data power allocation based on the derived rate expressions is formulated. Since the optimization problem has a non-linear and non-convex objective function, it is intractable to be solved straightforwardly. Therefore, we resort to utilizing an iterative algorithm relying on fractional programming. The numerical results show that a stronger LOS component (i.e., larger Rician K-factor) between a typical CU and its serving BS leads to a significant improvement in the system performance, while it has less effect on the D2D network.

Cellular Network Enabled Energy-Harvesting Secure Communications for Full-Duplex D2D Links

This article investigates the secure transmissions for a wireless powered full-duplex (FD) device-to-device (D2D) overlay system. To coordinate the cellular downlink transmissions and the D2D transmissions, we propose a two-phase transmission protocol, where the first phase is dedicated to the transmissions of information and energy from the base station (BS) to its users and the devices, respectively, and the second phase is dedicated to information exchanging between the two devices over an FD link overheard by either active or passive eavesdroppers. In the case of active eavesdroppers, perfect channel state information (CSI) is assumed to be available, whereas for passive eavesdroppers, only statistic CSI is available. Both self-interference (SI) mitigation and BS-aided cooperative jamming are considered for the D2D secure transmissions. For system design, nonconvex power minimization problems under transmission rate constraints are formulated and convex approximated for efficiently obtaining numerical solutions. Simulation results show that the proposed scheme can well coordinate the cellular transmissions and the FD-D2D secure transmissions, while the cooperative jamming strategy can enhance the D2D transmission security, especially in the case of passive eavesdroppers.

Quantized Distributed Federated Learning for Industrial Internet of Things

Federated learning (FL) enables multiple devices to collaboratively train a shared machine learning (ML) model while keeping all the local data private, which is a crucial enabler to implement artificial intelligence (AI) at the edge of the Industrial Internet of Things (IIoT) scenario. Distributed FL (DFL) based on Device-to-Device (D2D) communications can solve the single point of failure and scaling issue of centralized FL, but subject to the communication resource limitation of D2D links. Thus, it is crucial to reduce the data transmission volume of FL models between devices. In this article, we propose a quantization-based DFL (Q-DFL) mechanism in a D2D network and prove its convergence. Q-DFL contains two phases: 1) in phase I, a local model is trained with the stochastic gradient descent (SGD) algorithm on each IIoT device and then exchanges the quantified model parameters between neighboring nodes and 2) in phase II, a quantitative consensus mechanism is designed to ensure the local models converge to the same global model. We also propose an adaptive stopping mechanism and a synchronization protocol to fulfill the phase transition from phase I to phase II. Simulation results reveal that with Q-DFL, a 1-bit quantizer can be employed without affecting the model convergence at the price of slight accuracy reduction, which achieves significant transmission bandwidth saving. Further simulation of Q-DFL for the MobileNet model is fulfilled with different quantization bit levels, which reveals their performance tradeoff among the system information flow consumption, the system time delay, and the system energy cost.

A Bidirectional Relay-Assisted Underlay Device-to-Device Communication in Cellular Networks: An IoT Application for FinTech

The device-to-device (D2D) networks bear a close resemblance to future Internet-of-Things (IoT) networks. IoT plays a significant role in the FinTech industry, especially in data security and context-aware applications. D2D communication in cellular networks has emerged as a competent technology for the upcoming future cellular networks. To improve spectral efficiency and data secrecy, we propose a two-phase network coding-based two-way decode-and-forward (DF) relaying policy for D2D communication. First, a suitable relay node is selected considering the end-to-end signal-to-interference-plus-noise ratio (SINR). Then, an XOR-based DF operation is performed when the signal transmitted by both the D2D users (DUEs) is successfully decoded at the relay node. In order to analyze the performance of the proposed two-way communication system, we derive the outage probability expression as a function of various system parameters, such as the SINR, position of both the relays and DUEs, power splitting factor, number of relays, and data rate. Furthermore, we also derive an expression for the error probability and average throughput of the D2D link to evaluate a fair comparison of the proposed approach with the traditional methods. The analytical results are also validated with the simulation results to justify the efficacy of the proposed method.

Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

Covertness and Secrecy Study in Untrusted Relay-Assisted D2D Networks

This article investigates the covertness and secrecy of wireless communications in an untrusted relay-assisted device-to-device (D2D) network consisting of a full-duplex base station (BS), a user equipment (UE), and an untrusted relay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> . For the covertness, we attempt to prevent Willie from detecting the very existence of communications via a D2D link from UE to R and cellular link from R to BS, while for the secrecy, we aim to prevent the untrusted relay from eavesdropping the UE message. To explore the fundamental covertness and secrecy in such a network, we first provide theoretical modelings for the average minimum detection error rate of Willie, and the average covert/secrecy rate from UE to BS under the underlay and overlay modes, respectively. Based on these models, th we further explore the optimal power control at UE, R, and BS to achieve the average covert rate maximization (MCR) for UE with the constraints of covertness and security requirements under the underlay mode. We also identify the optimal transmit powers and the optimal spectrum partition factor for MCR under the overlay mode. Finally, the exhaust searching method is adopted to solve the MCR problems, and extensive numerical and simulation results are presented to validate our theoretical analysis and to illustrate the average covert rate and secrecy rate of UE under various scenarios.