Optimal Multi-Objective Resource Allocation for D2D Underlaying Cellular Networks in Uplink Communications

This paper considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity resource allocation algorithm to strike a balance between performance and complexity. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon's capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they may violate the users' delay constraints.

This paper investigates the resource allocation design in device‐to‐device (D2D) communication underlying cellular networks, which is assisted by multiple intelligent reflecting surfaces (IRSs) deployed at the cell boundary to enhance desired signals and mitigate interference between D2D pairs and CUs. In this regard, a multi‐objective optimization problem (MOOP) framework is formulated to jointly maximize the downlink sum‐rate of the D2D pairs and cellular users (CUs). To doing so, the MOOP is first converted into a single‐objective optimization problem (SOOP) by invoking the weighted sum method. Next, to facilitate the high‐coupled non‐convex SOOP formulation, it is decomposed into two sub‐problems through the alternative optimization method. For the first sub‐problem, the inner approximation and successive convex approximations are adopted for jointly allocating D2D transmission power, subcarrier assignment, and transmit beamforming matrix at the BS. For the second sub‐problem, an iterative algorithm based on the penalty‐based method as well as Fenchel's duality approach is adopted to design the passive beamforming matrices at IRSs, which is guaranteed to rapidly converge to a stationary point. Simulation results reveal that a non‐trivial trade‐off between the total throughput of CUs and D2D pairs exist. Furthermore, the proposed framework significantly outperforms existing works addressed in the literature.

This paper proposes orthogonal frequency division multiple access (OFDMA) based multiuser hybrid cooperative- D2D (C-D2D) communication system with the best user selection. In the proposed model, the listen-before-talk (LBT) algorithm with an energy detection method has been used to check the WiFi spectrum availability. Based on the spectrum availability, the system adapts to any one of the two modes, namely in-band C- D2D or out-band C-D2D. Further, the best user selection scheme is applied to a system wherein multiple D2D users coexist with a cellular user in the same cell. In particular, among the M available D2D pairs, selected D2D user helps the cellular user by relaying D out of N subcarriers to the base station (BS). Based on the availability of Wi-Fi, either N-D subcarriers of the cellular spectrum or N subcarriers of the Wi-Fi spectrum will be shared among M D2D users for D2D transmission. Closedform expressions of outage probability for cellular and D2D users are derived. Results show that the proposed hybrid C-D2D communication system with the best user selection outperforms the conventional C-D2D framework.

<p>Device-to-device (D2D) communication can effectively use cognitive radio network approach to coexist with cellular users. For such a cognitive D2D system, two approaches (underlay and overlay) are considered to manage the spectrum sharing among the cellular (primary) users and the D2D (secondary) users. Energy efficiency (EE) is crucial in both these cases due to limited battery capacity and quality of service requirements of the D2D users. This study effectively models the power allocation problem of such a cognitive D2D system by maximising the EE of the D2D users subject to a minimum rate requirement for both the D2D users and the cellular users. This leads to a non-linear fractional optimisation problem which is more complicated and computationally intractable. Alternatively, geometric water-filling approach have been utilised for power allocation to solve this optimisation problem which results in an ‘exact’ and ‘low complexity’ solution. Simulation results reveal the benefits of the proposed algorithm.</p> <p><br></p>

This paper investigates the resource management problem for device-to-device (D2D) underlaying cellular network. In particular, cellular users employ sparse code multiple access (SCMA) while D2D users employ orthogonal frequency division multiple access (OFDMA). In this system, the cross-tier interference between cellular users and D2D users is highly coupled and if not properly handled, the potentials of the two 5G key enabling technologies (i.e., SCMA and D2D) cannot be fully exploited. To tackle this problem, we first construct an interference graph to depict the cross-tier interference, based on which an efficient resource allocation algorithm is devised to well coordinate the interference. Then, we propose an easily implemented iterative power control scheme to optimize the transmit power of all users. Finally, simulation results exhibit the performance gain of our algorithms as compared with other schemes.

The performance of device-to-device (D2D) communication in a cellular network depends on the resource sharing between D2D links and cellular users. Existing researches on resource sharing mainly focus on power control between the D2D users and cellular users that operate in the same frequency band. However, the D2D outage probability performance is hampered by the cellular interference to D2D links. Therefore, the D2D users may not achieve satisfactory SINR performance when D2D users and cellular users are geographically located in a small area; as a result, the outage probability performance would be significantly degraded. In this paper, we provide a novel resource sharing strategy to mitigate the interference from cellular users to D2D receivers by utilizing the low energy characteristics of signals in the guard band and analyze the D2D outage probability performance mathematically. Both the mathematical analysis and numerical results show that the proposed resource sharing strategy provides 1.2 dB SNR gain in D2D outage probability performance while guaranteeing the cellular throughputs.

<p>Device-to-device (D2D) communication is developed as a new paradigm to enhance network performance according to LTE and WiMAX advanced standards. The D2D communication may have dedicated spectrum (overlay) or shared spectrum (underlay). However, the allocated dedicated spectrum may not be effectively used in the overlay mode, while interference between the D2D users and cellular users cause impairments in the underlay mode. Can the resource allocation of a D2D system be optimized using the cognitive approach where the D2D users opportunistically access the underutilized radio spectrum? That is the focus of this paper. In this paper, the transmission rate of the D2D users is optimized while simultaneously satisfying five sets of constraints related to power, interference, and data rate, modeling D2D users as cognitive secondary users. Furthermore, a two-stage approach is considered to allocate the radio resources efficiently. A new adaptive subcarrier allocation scheme is designed first, and then, a novel power allocation scheme is developed utilizing geometric water-filling approach that provides optimal solution with low computation complexity for this nonlinear problem. Numerical results show that the proposed approach achieved significant performance enhancement than the existing schemes.</p> <p> </p>

The space-terrestrial integrated network (STIN) is crucial for achieving ubiquitous connectivity in the 6G era. However, leveraging full potential of STIN is challenging due to the distinct characteristics and objectives of constituent networks. Inspired by symbiotic communication (SC), this paper proposes a service exchange-based symbiotic STIN system that optimizes objectives of different networks by exploiting their complementary features. Specifically, the ground network provides task offloading services to the space network, while the space network reciprocates with communication services. To minimize computation delay in the space network and maximize the energy efficiency (EE) of the ground network, we formulate a multi-objective optimization problem (MOOP) that jointly optimizes task offloading, resource allocation, and beamforming. We first transform the MOOP into a single-objective optimization problem (SOOP) via the ε-constraint method and then develop a successive convex approximation (SCA) algorithm to characterize its fundamental performance, which requires future state information. As obtaining such non-causal information is hard, we design a more practical multi-agent reinforcement learning (MARL) algorithm based on insights from the SCA. Besides, to address the challenges of storing multiple MARL policies for different EE-delay trade-offs, we develop a diffusion model-based behavior cloning (BC) algorithm to obtain a general policy suitable for varying trade-offs. Simulation results show that proposed algorithms outperform benchmarks and confirm that the proposed service exchange realizes a symbiotic STIN.

In this paper, downlink joint user association, codebook assignment and power allocation for a Sparse Code Multiple Access (SCMA)‐based in Cloud Radio Access Network (C‐RAN) are investigated. The main aim of this paper is to accomplish a resource allocation to achieve the maximum total sum rate subject to SCMA limitation, total available power in Radio Remote Head (RRH), fronthaul constraint, users association, user power constraint, and Quality of Service requirements for each user with low complexity. To solve the recommended problem an iterative algorithm considering Successive Convex Approximation is utilized. The main problem is separated into two subproblems of joint user association and codebook assignment and power allocation. By defining a new binary variable for user association and codebook assignment and a new constraint for the user association, the joint user association and codebook assignment subproblem becomes an Integer Linear Programming. Then user association and codebook assignment subproblem solved with the help of the CVX toolbox and MOSEK solver. Furthermore, to solve the power allocation subproblem, Successive Convex Approximation with Low Complexity is applied. Then the power allocation subproblem solved by subgradient method for the Lagrange dual problem. In the simulation results, the effectiveness of the recommended optimization algorithm and resource allocation method are shown and compared to Non‐Orthogonal Multiple Access and Orthogonal Frequency Division Multiple Access.

Recently, device-to-device (D2D) communication and full duplex communication are both considered key technologies for 5G networks. In this paper, two novel cooperative modes are developed for full duplex D2D communication underlaying cellular networks: the network MU-MIMO based mode (N-mode) and the sequential forwarding mode (S-mode). In the N-mode, two D2D users work as network MIMO to forward the data to cellular users and thus leverage the channel diversity. In the S-mode, the spatial distribution of D2D users and cellular users is explored to improve the transmission rate. Based on these modes, two D2D users share the downlink resources with two nearby cellular users simultaneously to achieve both proximity gain and reuse gain. Moreover, these modes are well suited for edge cellular users. To optimize the performance of the two modes, optimal power allocation is conducted by considering the influence of residual self-interference at full duplex radios and the requirements of the minimum transmission rate for both cellular users and D2D users. Simulation results show that, compared with the dedicated mode, the sum rate of cooperative modes in the perfect self-interference cancellation case can achieve 32% overall improvement and about 70% improvement in the scenario with non-edge D2D users and edge cellular users. With residual self-interference at a full duplex radio, the overall performance improvement is reduced to 15%. However, the cooperative modes can still achieve 40% improvement in the scenario with non-edge D2D users and edge cellular users.

Device to device (D2D) communication technology is widely considered in 5G for providing higher data rates and increase network capacity. The performance benefits of D2D communication are best achieved if it takes place in shared mode in which it reuses the spectrum being utilized by conventional cellular users. This induces significant challenges in allocating resources because of severe interference among D2D and cellular users. Moreover, centralized resource allocation techniques proposed in literature for D2D users can no longer be practical in dense heterogeneous networks considered for 5G. In this paper, we present a distributed learning based spectrum allocation scheme in which D2D users learn the environment and autonomously select spectrum resources to maximize their Throughput and Spectral Efficiency (SE) while caus- ing minimum interference to the cellular users. We have employed distributed learning in a stochastic geometry based realistic network. Our evaluation results show that the employed learning scheme enables users to achieve high Throughput and Spectral Efficiency while meeting QoS requirements of macro and femto tier.

Device-to-device (D2D) communications allows two adjacent mobile terminals transmit signal directly without going through base stations, which has been considered as one of the key technologies for future mobile networks. As full-duplex (FD) communications can improve the performance (i.e., throughput, energy efficiency (EE)) of communications systems, it is commonly used in practical D2D communications scenarios. However, FD-enabled D2D communications also results in self-interference. To fully realize the potential benefits of FD-enabled D2D communications, an effective resource allocation mechanism is critical to avoid not only the self-interference of FD-enabled D2D communications but also the interference between D2D users (DUs) and cellular users (CUs). In this paper, we investigate the resource allocation issue for FD-enabled DUs and traditional CUs. Considering the asymmetry of energy and spectrum resources of DUs and CUs, we propose a power-spectrum trading mechanism to achieve mutual benefits for both types of users. A concave-convex procedure algorithm is employed to solve the optimization problem of power allocation, and then a maximum weighted bipartite matching based method is proposed to select proper D2D pairs to maximize the overall system throughput. Numerical results show that the proposed scheme can remarkably improve the overall throughput and EE of FD-enabled D2D communications system.

In this paper, we investigate resource allocation in a downlink cooperative communication system. In the scenario where cellular users (CUs) and the base station (BS) cannot communicate directly, D2D user pairs participate in the cooperative communication to complete forwarding information required by CU and themselves through non-orthogonal multiple access (NOMA). The application of simultaneous wireless information and power transfer (SWIPT) ensures that D2D users save their own energy during the forwarding process. The ultimate optimization goal is to maximize the achievable rate of D2D user pairs under the condition of guaranteeing CU's quality of service (QoS). A stepwise iterative algorithm is proposed to obtain the suboptimal solution to the problem. By comparing with the ergodic optimal solution, numerical simulations show that the proposed algorithm can approximate the optimal solution with low computational cost in a certain error range.

<p>Device-to-device (D2D) communication is developed as a new paradigm to enhance network performance according to LTE and WiMAX advanced standards. The D2D communication may have dedicated spectrum (overlay) or shared spectrum (underlay). However, the allocated dedicated spectrum may not be effectively used in the overlay mode, while interference between the D2D users and cellular users cause impairments in the underlay mode. Can the resource allocation of a D2D system be optimized using the cognitive approach where the D2D users opportunistically access the underutilized radio spectrum? That is the focus of this paper. In this paper, the transmission rate of the D2D users is optimized while simultaneously satisfying five sets of constraints related to power, interference, and data rate, modeling D2D users as cognitive secondary users. Furthermore, a two-stage approach is considered to allocate the radio resources efficiently. A new adaptive subcarrier allocation scheme is designed first, and then, a novel power allocation scheme is developed utilizing geometric water-filling approach that provides optimal solution with low computation complexity for this nonlinear problem. Numerical results show that the proposed approach achieved significant performance enhancement than the existing schemes. </p>

Sparse code multiple access (SCMA) has been recently proposed for the future wireless networks, which allows non-orthogonal spectrum resource sharing and enables system overloading. In this paper, we apply SCMA into device-to-device (D2D) communication and cellular hybrid network, targeting at using the overload feature of SCMA to support massive device connectivity and expand network capacity. Particularly, we develop a stochastic geometry based framework to model and analyze SCMA, considering underlaid and overlaid mode. Based on the results, we analytically compare SCMA with orthogonal frequency-division multiple access (OFDMA) using area spectral efficiency (ASE) and quantify closed-form ASE gain of SCMA over OFDMA. Notably, it is shown that system ASE can be significantly improved using SCMA and the ASE gain scales linearly with the SCMA codeword dimension. Besides, we endow D2D users with an activated probability to balance cross-tier interference in the underlaid mode and derive the optimal activated probability. Meanwhile, we study resource allocation in the overlaid mode and obtain the optimal codebook allocation rule. It is interestingly found that the optimal SCMA codebook allocation rule is independent of cellular network parameters when cellular users are densely deployed. The results are helpful in the implementation of SCMA in the hybrid system.