Power Allocation for Massive MIMO Cognitive Radio Networks With Pilot Sharing Under SINR Requirements of Primary Users

Hao, Wanming; Muta, Osamu; Gacanin, Haris; Furukawa, Hiroshi

doi:10.1109/tvt.2017.2749599

Cited by 20 publications

(13 citation statements)

References 37 publications

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“…where f * = 0 for ǫ = 0 and f * is non-zero for any ǫ > 0. When solving (15), the cells with t l = 0 are effectively removed from the objective function, while the remaining cells will have t l ′ > 0 at the optimal point. We define 1 ǫ = 1 + ǫ to simplify the notation in the remainder of the paper.…”

Section: A Proposed: Geometric-mean Per-cell Max-min Fairnessmentioning

confidence: 99%

Enhanced Fairness and Scalability of Power Control Schemes in Multi-Cell Massive MIMO

Ghazanfari

Cheng

Björnson

et al. 2020

IEEE Trans. Commun.

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This paper studies the transmit power optimization in multi-cell massive multiple-input multiple-output (MIMO) systems. Network-wide max-min fairness (NW-MMF) and networkwide proportional fairness (NW-PF) are two well-known power control schemes in the literature. The NW-MMF focus on maximizing the fairness among users at the cost of penalizing users with good channel conditions. On the other hand, the NW-PF focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. However, both of these schemes suffer from a scalability issue which means that for large networks, it is highly probable that one user has a very poor channel condition, pushing the spectral efficiency (SE) of all users towards zero. To overcome the scalability issue of NW-MMF and NW-PF, we propose a novel power control scheme that is provably scalable. This scheme maximizes the geometric mean (GM) of the per-cell max-min SE. To solve this new optimization problem, we prove that it can be rewritten in a convex optimization form and then solved using standard tools. The simulation results highlight the benefits of our model which is balancing between NW-PF and NW-MMF.

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Section: A Proposed: Geometric-mean Per-cell Max-min Fairnessmentioning

confidence: 99%

Enhanced Fairness and Scalability of Power Control Schemes in Multi-Cell Massive MIMO

Ghazanfari

Cheng

Björnson

et al. 2020

IEEE Trans. Commun.

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show abstract

“…improving spectrum efficiency. Other benefits of mMIMO are that (1) antennae in mMIMO deployments are designed to consume low levels of power, (2) the quality of the sensing results (i.e., channel state information) is improved as an increased number of antennae sense/observe the same channel simultaneously, and (3) the technology can be integrated with small cell networks and various other spatial spectrum reuse strategies [41] .…”

Section: Massive Mimomentioning

confidence: 99%

Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective

Rapetswa

Liu

2020

Intell. and Converged Netw.

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With next generation networks driving the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are positioned as a preferred paradigm to address spectrum capacity challenges. CRs address these issues through dynamic spectrum access. However, the main challenges faced by the CR pertain to achieving spectrum efficiency. As a result, spectrum efficiency improvement models based on spectrum sensing and sharing models have attracted a lot of research attention in recent years, including CR learning models, network densification architectures, and massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper provides a survey of recent CR spectrum efficiency improvement models and techniques, developed to support ultra-reliable low latency communications that are resilient to surges in traffic and competition for spectrum. These models and techniques, broadly speaking, enable a wide range of functionality ranging from enhanced mobile broadband to large scale Internet of Things (IoT) type communications. In addition and given the strong correlation between the typical size of a spectrum block and the achievable data rate, the models studied in this paper are applicable in ultra-high frequency band. This study therefore provides a good review of CRs and direction for future investigations into newly identified 5G research areas, applicable in industry and in academia.

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“…In [11], a cooperative relay selection rule by exploiting spatial diversity is proposed to improve the performance of interference-constrained SUs and achieve the maximum fading-averaged transmission rate in the cooperative CR network. In [13], the orthogonal pilot sharing scheme is proposed and power allocation strategy under the PUs' signal-interference-plus-noise-ratio constraint is investigated to maximize the sum rate of the secondary network. Obviously, the interference power constraint has become one of the most effective methods to protect the quality of service (QoS) of PU in CR network.…”

Section: To Utilizementioning

confidence: 99%

Resource Allocation in Multi-User Cognitive Radio Network With Stackelberg Game

Ning

Sun

et al. 2020

IEEE Access

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Resource allocation with sensing-based interference price is considered for multi-users cognitive radio (CR) network, in which the primary base station (PBS) controls the secondary users' (SUs) transmission by pricing the SUs' interference power. SUs firstly initiates data transmission based on the sensing decision and then PBS sets the interference price according to each SU's interference power. Stackelberg game is formulated to jointly obtain the maximum revenue for PBS and optimize the resource allocation to maximize the transmission gain for SUs. Two practical CR network models are investigated: the sensing based spectrum sharing(SBSS) and the opportunistic spectrum access(OSA). For each scenario, the resource allocation strategy is investigated under the two pricing schemes, namely uniform interference pricing and non-uniform interference pricing. Especially, the stackelberg equilibriums for the proposed games is characterized, and the distributed sensing based interference price bargaining algorithm is proposed according to different channel state information (CSI) for the non-uniform interference pricing case. Numerical examples are carried out to demonstrate the effectiveness of the proposed game algorithm under different pricing scheme. INDEX TERMS Resource allocation, Stackelberg game, cognitive radio networks, interference management. BING NING (Member, IEEE) received the B.E. degree in electronic information engineering from the Nanjing Artillery Academy of PLA, Nanjing, China, in 2009, and the Ph.D. degree in information and communication system from

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Power Allocation for Massive MIMO Cognitive Radio Networks With Pilot Sharing Under SINR Requirements of Primary Users

Cited by 20 publications

References 37 publications

Enhanced Fairness and Scalability of Power Control Schemes in Multi-Cell Massive MIMO

Enhanced Fairness and Scalability of Power Control Schemes in Multi-Cell Massive MIMO

Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective

Resource Allocation in Multi-User Cognitive Radio Network With Stackelberg Game

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