A Two-Stage Energy-Efficient Approach for Joint Power Control and Channel Allocation in D2D Communication

Abstract

A large number of mobile multimedia terminals are prominent features of smart cities. Device-to-device (D2D) communication takes advantage of the limited bandwidth resources of cellular networks to accommodate more mobile devices. However, when D2D pairs reuse cellular users channels, serious interference leads to energy consumption, which dissatisfies the requirements of green communication. This paper focuses on energy efficiency maximization of D2D communication under the constraints of both D2D pairs and cellular users quality of service. The formulated resource allocation problem is NP-hard, which is usually difficult to solve within polynomial time. To make the problem easy to handle, we divide it into power control and channel allocation sub-problems. In particular, we propose a power control algorithm based on the Lambert W function to maximize the energy efficiency of a single D2D pair. The preference values of D2D pairs and cellular users are calculated using the power control results, respectively. A channel allocation scheme based on the Gale-Shapley algorithm utilizes preference values to match two sides, which aims at maximizing the signal to interference plus noise ratio of cellular users and the energy efficiency of D2D pairs. The simulation results show that the proposed algorithm could not only guarantee the transmission rate of cellular users but also improve the system and D2D pairs energy efficiency.INDEX TERMS Device-to-device (D2D) communication, cellular networks, energy efficiency, resource allocation, green communications.

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

“…Furthermore, it only briefly discusses the interference information between users and ignores the power collaboration between the D2D pairs and CUs. Based on game theory, the computational complexity of the channel allocation in References [ 30 , 31 ] is related to the number of iterations and the suboptimal solution or the optimal solution is ultimately obtained. The channel allocation proposed in this study can obtain the optimal channel-matching scheme through the Kuhn–Munkres algorithm with complexity .…”

“…The D2D communication network architecture should meet the quality of service (QoS) requirements of the CUs as well as solve EE optimization problems [14]. Liu et al [30] focused on the EE maximization problem with the QoS constraints of both the CUs and D2D pairs in mind. After the transmission power is allocated, game theory is utilized to establish the preferences of each user's equipment and obtains a stable matching result.…”

“…where p d0 th is the minimum power limit of p d m , and p B th is the minimum power limit of p B . By observing Equations (11) and (30), it can be concluded that the smaller the value of p B , the higher the EE. Therefore, when the maximum value of η ee is obtained, p B must be its minimum value * p B , which is expressed by:…”

“…Stable matching algorithm reusing the uplink spectrum resources [30]: This algorithm allocates optimal transmission power to the D2D pairs. Then, the channel gain ratio of the communication link and the interference link is defined as the sequence value of the user-matching preference.…”

Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications

“…Meanwhile, in [13], in order to maximise the total average achievable rate from D2D transmitters to D2D receivers, the authors proposed an optimal solution to allocate the spectrum and power in cooperative D2D communications with multiple D2D pairs. In [14], a resource allocation approach was presented to improve energy-efficient D2D communication.…”