41 research outputs found
Resource Allocation for Device-to-Device Communications in Multi-Cell Multi-Band Heterogeneous Cellular Networks
Heterogeneous cellular networks (HCNs) with millimeter wave (mm-wave)
communications are considered as a promising technology for the fifth
generation mobile networks. Mm-wave has the potential to provide multiple
gigabit data rate due to the broad spectrum. Unfortunately, additional free
space path loss is also caused by the high carrier frequency. On the other
hand, mm-wave signals are sensitive to obstacles and more vulnerable to
blocking effects. To address this issue, highly directional narrow beams are
utilized in mm-wave networks. Additionally, device-to-device (D2D) users make
full use of their proximity and share uplink spectrum resources in HCNs to
increase the spectrum efficiency and network capacity. Towards the caused
complex interferences, the combination of D2D-enabled HCNs with small cells
densely deployed and mm-wave communications poses a big challenge to the
resource allocation problems. In this paper, we formulate the optimization
problem of D2D communication spectrum resource allocation among multiple
micro-wave bands and multiple mm-wave bands in HCNs. Then, considering the
totally different propagation conditions on the two bands, a heuristic
algorithm is proposed to maximize the system transmission rate and approximate
the solutions with sufficient accuracies. Compared with other practical
schemes, we carry out extensive simulations with different system parameters,
and demonstrate the superior performance of the proposed scheme. In addition,
the optimality and complexity are simulated to further verify effectiveness and
efficiency.Comment: 13 pages, 11 figures, IEEE Transactions on Vehicular Technolog
Power allocation for D2D communications using max-min message-passing algorithm
The approach of factor-graphs (FGs) is applied in the context of power control and user pairing in Device-to-Device (D2D) communications as an effective underlay concept in wireless cellular networks. D2D communications can increase the spectral efficiency of wireless cellular networks by establishing a direct link between devices with limited help from the evolved node base stations (eNBs). A well-designed user pairing and power allocation scheme with low complexity can remarkably improve the system’s performance. In this paper, a simple and distributed FG based approach is utilized for power control and user pairing implementation in an underlay cellular network with D2D communications. A max-min criterion is proposed to maximize the minimum rate of all active users in the network, including the cellular and multiple D2D co-channel links in the uplink direction. An associated message-passing (MP) algorithm is presented to distributedly solve the resultant NP-hard maximization problem, with a guaranteed convergence compared to game-theoretic and Q-learning based methods. The complexity and convergence of the proposed method are analyzed and numerical results confirm that the proposed scheme outperforms alternative algorithms in terms of complexity, while keeping the sum-rate of users nearly the same as centralized counterpart methods
Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC
Mission-critical communication (MCC) is one of the main goals in 5G, which can leverage multiple device-to-device (D2D) connections to enhance reliability for mission-critical communication. In MCC, D2D users can reuses the non-orthogonal wireless resources of cellular users without a base station (BS). Meanwhile, the D2D users will generate co-channel interference to cellular users and hence affect their quality-of-service (QoS). To comprehensively improve the user experience, we proposed a novel approach, which embraces resource allocation and power control along with Deep Reinforcement Learning (DRL). In this paper, multiple procedures are carefully designed to assist in developing our proposal. As a starter, a scenario with multiple D2D pairs and cellular users in a cell will be modeled; followed by the analysis of issues pertaining to resource allocation and power control as well as the formulation of our optimization goal; and finally, a DRL method based on spectrum allocation strategy will be created, which can ensure D2D users to obtain the sufficient resource for their QoS improvement. With the resource data provided, which D2D users capture by interacting with surroundings, the DRL method can help the D2D users autonomously selecting an available channel and power to maximize system capacity and spectrum efficiency while minimizing interference to cellular users. Experimental results show that our learning method performs well to improve resource allocation and power control significantly.This work has been supported by the National Natural Science Foundation of China (Nos. 61772387 , 62071354 ), the National Natural Science Foundation of Shaanxi Province, China (Grant Nos. 2019ZDLGY03-03 ), Graduate Student Innovation Fund ( 10221150004 ), and also supported by the ISN State Key Laboratory