Compressive Sensing Based Spectrum Allocation and Power Control for NOMA HetNets

Nasser, Ahmed; Muta, Osamu; Elsabrouty, Maha; Gacanin, Haris

doi:10.1109/access.2019.2929185

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…Perfect CSI: The RA problems with the overall throughput maximization have been studied in [18]- [20]. For instance, in [18], a joint user scheduling and power control algorithm was proposed to maximize the overall throughput for downlink NOMA-based HetNets.…”

Section: A Related Workmentioning

confidence: 99%

“…For a downlink NOMA-based heterogeneous cognitive network with one macrocell and multiple SCs [19], the sum throughput of multiple SCs was maximized by jointly optimizing bandwidth allocation, power allocation, and user clustering. In [20], a compressive sensing based spectrum allocation and power control algorithm was proposed to achieve the overall sum-rate maximization while considering both co-tier and cross-tier interferences. To improve data rate and reduce power consumption, EE-based RA problems have been studied in [21]- [24] according to different network scenarios.…”

Section: A Related Workmentioning

confidence: 99%

“…19: f = f + 1, update η(f ) using (21). 20: end while sensitivity and the impact of CSI error on outage probability.…”

Section: Performance Analysismentioning

confidence: 99%

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Joint User Association and Power Allocation in Heterogeneous NOMA Networks With Imperfect CSI

Wang

et al. 2020

IEEE Access

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Non-orthogonal multiple access (NOMA)-enabled heterogeneous networks (N-HetNets) can provide higher system capacity and spectrum efficiency by allowing multiple users cooperating in the same channel. However, traditional resource allocation algorithms are achieved under perfect channel state information (CSI) which is difficult to obtain in practical systems. In this paper, we study the total energy efficiency maximization problem in a downlink multicell N-HetNets under imperfect CSI, where the constraints of the quality of service of small-cell users, the maximum transmit power, and the cross-tier interference are considered. With the help of the ellipsoidal uncertainty sets, an iterative-based power allocation and user association algorithm is proposed based on the worst-case approach and the Lagrange dual method. Moreover, computational complexity, robust sensitivity and the impact of imperfect CSI error on user's outage probability are provided to insight the performance of the proposed algorithm. Simulation results demonstrate the robustness of the proposed algorithm.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

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Joint User Association and Power Allocation in Heterogeneous NOMA Networks With Imperfect CSI

Wang

et al. 2020

IEEE Access

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“…aggressive frequency reuse results in severe inter-cell interference (ICI) and, thus, significantly limits the network performance gain [3], [4]. In downlink (D L ) transmission, for instance, SBSs strongly interfere with MBS-AUs in the proximity of SBS coverage.…”

Section: Introduction a Motivationmentioning

confidence: 99%

Proactive Uplink Interference Management for Nonuniform Heterogeneous Cellular Networks

et al. 2020

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In homogeneous cellular networks, fractional power control (FPC) is employed to partially compensate the path-loss and, hence, improve uplink (U L) signal-to-interference ratio (SIR). However, this scheme is less effective in heterogeneous cellular networks (HetNets) because: (i) except the typical user, all other users with variable U L transmit power (UTP) act as interferers, (ii) FPC leads to high UTP by edge users and, hence, more interference, and (iii) small base stations (SBSs)' densification further increases network interferences. Leveraging FPC in HetNets, we propose nonuniform SBS deployment (NU-SBS D) to reduce interference and, thus, increase network performance. According to our NU-SBS D model, SBS deployment (SBS D) near macro base station (MBS) is avoided, whereas MBS coverage edge area is enriched with ultra-dense SBS D. NU-SBS D model leads to: (i) better SIR reception of MBS coverage edge users, (ii) fewer SBS D requirement, and (iii) better SBS coverage in the MBS coverage edge area. Moreover, to make a model more proactive, we also consider reverse frequency allocation (RFA) to further abate both U L and downlink (D L) interferences. The coverage probability expressions are derived for both uniform SBS deployment (U-SBS D) and NU-SBS D while using RFA and FPC. Through simulation and numerical results, we characterize coverage probability for different values of SIR threshold, path loss compensation factor, SBS density, users density, and the distance between the typical user and the associated base station. The proposed NU-SBS D model along with RFA leads to reduced network interference as compared with U-SBS D and, thus, leverages FPC in HetNets.

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“…In this paper, inspired from the previous works [ 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], referring to RL and the idea of converting non-convex NP hard problem into several sub-problems, a turbo QL (TQL) scheme is proposed to optimize energy efficiency in which the traditional QL algorithm is decomposed into several sub-Q-Learning algorithms and has a loop iteration structure, each sub-Q-learning solving each sub-problem. In our scheme, the parameters ABS, CRE, and SI-SBSs are jointly taken into account as action vectors, the user positions are taken as the states in order to fully consider the randomness of users in BSs, and the reward function is designed as a negative reciprocal of the system energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

Wang

et al. 2020

Entropy

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In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

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Compressive Sensing Based Spectrum Allocation and Power Control for NOMA HetNets

Cited by 18 publications

References 28 publications

Joint User Association and Power Allocation in Heterogeneous NOMA Networks With Imperfect CSI

Joint User Association and Power Allocation in Heterogeneous NOMA Networks With Imperfect CSI

Proactive Uplink Interference Management for Nonuniform Heterogeneous Cellular Networks

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

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