Dynamic Buffer Status-Based Control for LTE-A Network With Underlay D2D Communication

Asheralieva, Alia; Miyanaga, Yoshikazu

doi:10.1109/tcomm.2016.2523505

Cited by 19 publications

(15 citation statements)

References 36 publications

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“…An optimal dynamic mode selection and resource allocation to minimize the average delay subject to a dropping probability constraint in orthogonal frequencydivision multiple-access cellular networks with D2D has been developed in [18]. A delay-aware algorithm to solve the problem of joint dynamic mode selection, spectrum management, power control, and interference mitigation in D2D communications underlaying LTE-A networks with both instantaneous and non-instantaneous implementations has been explored in [19]. Although the delay-aware solutions in [16]- [19] can achieve significant performance improvement in conventional cellular networks with D2D, these resource allocation schemes can not be adopted to C-RANs with D2D since both the network-wide beamforming design and the impact of fronthaul capacity limitation must be explicitly taken into consideration for practical C-RANs.…”

Section: A Related Workmentioning

confidence: 99%

Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications

Peng

Xiang

et al. 2017

IEEE Access

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To alleviate the burdens on the fronthaul and reduce the transmit latency, the device-to-device (D2D) communication is presented in cloud radio access networks (C-RANs). Considering dynamic traffic arrivals and time-varying channel conditions, the resource allocation in C-RANs with D2D is formulated into a stochastic optimization problem, which is aimed at maximizing the overall throughput subject to network stability, interference, and fronthaul capacity constraints. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem is transformed into a delay-aware optimization problem, which is a mixed-integer nonlinear programming problem and can be decomposed into three subproblems: mode selection, uplink beamforming design, and power control. An optimization solution that consists of a modified branch and bound method as well as a weighted minimum mean square error approach has been developed to obtain the close-to-optimal solution. Simulation results validate that the D2D can improve throughput, decrease latency, and alleviate the burdens of the constrained fronthaul in C-RANs. Furthermore, an average throughput-delay tradeoff can be achieved by the proposed solution.Index Terms-Cloud radio access networks (C-RANs), radio resource allocation, device-to-device (D2D)

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Section: A Related Workmentioning

confidence: 99%

Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications

Peng

Xiang

et al. 2017

IEEE Access

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“…Consequently, we consider the network where K orthogonal channels, numbered C 1 , ..., C K , are available for D2D communication, with K = {1, …, K} being the set of the corresponding channel indices. Note that in the first scenario, only the D2D users are allowed to transmit over the channels from the set K. The cellular users operate on their dedicated channels belonging to some set K C , such that K C  K =  , and the total available network bandwidth consists of the channels from the union set K C  K. 2 In the second scenario, both the cellular and D2D users can transmit over the channels in the set K (that is, the total spectrum band of the network comprises the channels in the set K). …”

Section: A Network Modelmentioning

confidence: 99%

“…Adequate interference management and resource allocation schemes can boost the performance of D2D-enabled cellular networks (in terms of spectrum efficiency, cellular coverage, network throughput, user experience, etc.). The majority of existing approaches in this area (e.g., [2] - [8]) consider the network as a single entity, where the resources are allocated by some BS that has a global knowledge of the precise or statistical channel state information (CSI). However, since conventional pilot signals (deployed for cellular communication) cannot be used for estimation of the D2D channels [9], the assumption of the precise CSI in a D2D-enabled cellular network is somewhat unrealistic.…”

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confidence: 99%

An Autonomous Learning-Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks

Asheralieva

Miyanaga

2016

IEEE Trans. Commun.

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-We study the problem of autonomous operation of the device-to-device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs). The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: i) the D2D pairs transmit over the dedicated frequency bands, ii) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plusnoise ratio (SINR) requirements. We formulate this problem as a stochastic non-cooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to "learn" its best strategy (based on the locally observed information) and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium (NE). The proposed learning method is implemented in a Long Term Evolution-Advanced (LTE-A) network and evaluated via the OPNET-based simulations. The algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations.

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“…In theory, when channel state information (CSI) is assumed to be known by the decision center, several optimization methods could be proposed to solve the problem of channel and power allocation in D2D communications . In the work of Chien et al, a joint optimization problem for both mode selection and resource allocation is formulated to maximize the sum rate by considering intracell traffic based on mixed‐integer nonlinear programming.…”

Section: Introductionmentioning

confidence: 99%

“…In the work of Asheralieva and Miyanaga, the problem of resource allocation for D2D communications in a third generation partnership project (3GPP) LTE‐A network is investigated, where the corresponding optimization problem maximizes the quality of service (QoS) of the users by minimizing the size of the buffers of user equipments. In addition, the work of Asheralieva and Miyanaga explored the problem of joint mode selection, spectrum management, power control, and interference mitigation for D2D communications underlaying a LTE‐A network.…”

Section: Introductionmentioning

confidence: 99%

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

Rashed

Shahbazian

Ghorashi

2017

Trans Emerging Tel Tech

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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.

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Dynamic Buffer Status-Based Control for LTE-A Network With Underlay D2D Communication

Cited by 19 publications

References 36 publications

Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications

Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications

An Autonomous Learning-Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks

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

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