Learning‐based resource allocation in D2D communications with QoS and fairness considerations

Abstract

In device-to-device (D2D) communications, D2D users establish a direct link by utilizing the cellular users' spectrum to increase the network spectral efficiency. However, due to the higher priority of cellular users, interference imposed by D2D users to cellular ones should be controlled by channel and power allocation algorithms. Due to the unknown distribution of dynamic channel parameters, learning-based resource allocation algorithms work more efficient than classic optimization methods. In this paper, the problem of the joint channel and power allocation for D2D users in realistic scenarios is formulated as an interactive learning problem, where the channel state information of selected channels is unknown to the decision center and learned during the allocation process. In order to achieve the maximum reward function by choosing an action (channel and power level) for each D2D pair, a recency-based Q-learning method is introduced to find the best channel-power for each D2D pair. The proposed method is shown to achieve logarithmic regret function asymptotically, which makes it an order optimal policy, and it converges to the stable equilibrium solution. The simulation results confirm that the proposed method achieves better responses in terms of network sum rate and fairness criterion in comparison with conventional learning methods and random allocation.

Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication

“…Its autonomous and fast converging performance outperforms greedy, ‐greedy Q‐learning and ranked heuristic‐based algorithm in terms of the average user throughput. Multi‐state Markovian decision process (MDP) is used to model RB allocation in U‐D2D in [18]. Recency‐based Q ‐value updating function is proposed to prevent the quick convergence to local optimal policy in conventional Q‐learning algorithm.…”

Resource sharing and scheduling in device-to-device communication underlying cellular network

“…Where 𝑔 𝑖 (𝑚) and 𝑝 𝑖 (𝑚) are the channel gain and transmission power at the slot 𝑚, respectively, and the white Gaussian noise power is shown as 𝜎 2 . At the denominator of Equation ( 4), channel gain and transmission powers are denoted as 𝑝 𝑗 (𝑚) and ℎ 𝑗 (𝑚) for the interfering D2D pairs.…”

“…In the underlay model, D2D users and cellular users (CUs) can simultaneously share the uplink or downlink channels while in the overlay model there exist a dedicated resource for D2D either in uplink or downlink channel. In the literature, most of the studies considered underlay D2D communication due to its higher spectral gain over to overlay model [2]- [5]. In D2D communications that underlay the communication links of a cellular network, D2D users communicate directly with each other by sharing the radio resources with cellular users, either in a non-orthogonal or orthogonal manner [1].…”

“…Resource sharing problem in D2D underlay setting is studied extensively in the literature [2], [3], [14]- [17]. However all these studies focused on increasing sum rate of the network and all of them investigate the case where only one D2D pair is allowed to share the channel with CU.…”

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

“…Using the learning phenomenon, a strategy for allocation of resources to a D2D network is proposed in the work of Kazemi Rashed et al A low complexity algorithm has been proposed in the work of Cicalò and Tralli for maximizing the weighted sum rate. The number of D2D pairs and cellular users is bounded, which provides satisfactory service.…”