Dynamic Spectrum Sharing for Hybrid Access in OFDMA-Based Cognitive Femtocell Networks

Deng, Qingyong; Li, Zhetao; Chen, Jiabei; Zeng, Fanzi; Wang, Huiming; Zhou, Liang; Choi, Young‐June

doi:10.1109/tvt.2018.2869755

Cited by 27 publications

(16 citation statements)

References 44 publications

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“…To combat the limited sensing capability, enforced self-learning method has been adopted to smooth the dynamic spectrum access [27]. From the perspective of whole networks, fully using spectrum reuse to enhance spectrum efficiency has drawn extensive attention [28]. Numbers of mathematical tools including graph theory, game theory and intelligent optimization algorithm have been utilized to optimize dynamic spectrum access and allocation [29].…”

Section: Spectrum-related Functionalitiesmentioning

confidence: 99%

Advances and Emerging Challenges in Cognitive Internet-of-Things

Lam

et al. 2020

IEEE Trans. Ind. Inf.

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The evolution of IoT devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e. IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of the research community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection and heterogeneous coexistence. In this paper, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitiveradio-based IoT networks .

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Section: Spectrum-related Functionalitiesmentioning

confidence: 99%

Advances and Emerging Challenges in Cognitive Internet-of-Things

Lam

et al. 2020

IEEE Trans. Ind. Inf.

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“…Consequently, a narrow-band clustered channel representation based on the extended Saleh-Valenzuela model is usually adopted [38]- [40], where the channel matrix H is assumed to be a sum of the contributions of N cl different clusters, each of which has N ray propagation paths. Accordingly, the channel model of mmWave massive MIMO systems can be abstracted as (4), where γ is a normalization factor, α il is the complex gain of the l th propagation path in the i th cluster. Functions r (•) and t (•) represent the gains of transmit and receive antenna, respectively, that correspond to different departure and arrival angles.…”

Section: B Cluster-based Channel Modelmentioning

confidence: 99%

Hybrid Precoding for Massive mmWave MIMO Systems

Liu

Cao

et al. 2019

IEEE Access

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Due to high costs and power consumptions, fully digital baseband precoding schemes are usually prohibitive in millimeter-wave massive MIMO systems. Therefore, hybrid precoding strategies become promising solutions. In this paper, we present a novel real-time yet high-performance precoding strategy. Specifically, the eigenvectors corresponding to the larger eigenvalues of the right unitary matrix after singular value decomposition on an array response matrix are used to abstract the angle information of an analog precoding matrix. As the obtained eigenvectors correspond to the larger singular values, the major phase information of channels is captured. In this way, the iterative search process for obtaining the analog precoding vectors is avoided, and thus the hybrid precoding can be realized in parallel. To further improve its spectral-efficiency, we enlarge the resultant vector set by involving more relevant vectors in terms of their correlation values with the unconstrained optimal precoder, and a hybrid precoder is thus produced by using the vector set. The simulation results show that our proposed scheme achieves near the same performance as the orthogonal matching pursuit does, whereas it costs much fewer complexities than the OMP, and thus can be realized in parallel. INDEX TERMS Millimeter wave communication, MIMO, wireless communication, hybrid precoding. I. INTRODUCTION

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“…Zhang et al [9] proposed a spectrum allocation method for hybrid-access femtocells where a macro BS allocates a portion of subchannels to femtocells with which each femtocell guarantees the throughput of served macrocell users. Deng et al [10] proposed a similar framework and designed an auction-based algorithm where each femtocell receives additional macro users as bidders while the macro BS acts as the auctioneer. Thakur et al [11] developed a QoS-aware cell selection scheme that assigns mobile users to femtocells, incorporating the energy consumption of backhaul links.…”

Section: Limitations Of Existing Techniquesmentioning

confidence: 99%

Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells

Yun

2019

Applied Sciences

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Femtocell technology has been gaining popularity as an inexpensive means of enhancing cellular coverage and capacity. However, under co-channel deployment—expected to be commonplace due to high spectrum costs—femtocells may incur harmful interference to existing macrocells, and vice versa. To alleviate this problem, This paper proposes a new architecture called HandOver-driven Femtocell Interference Management (HO-FIM) that offloads macrocells by handing macrocell users over to “hybrid-access” femtocells that allow the access of both subscribed and unsubscribed users. This use of handovers adds a new degree-of-freedom for solving the interference problem, thus overcoming the capacity limit of the existing approaches that exploit radio resource coordination only. A new challenge associated with this approach is how to minimize handovers while reaching a desired system condition due to the inevitable service interruptions that accompany handovers. This paper proposes to overcome this challenge by triggering handovers only when needed and, once triggered, to find an optimal sequence to reach a target system condition with minimal handovers. This paper designs a greedy per-user control algorithm that has a fine control granularity and thus enables convergence to an optimal solution. The in-depth evaluation of the paper shows that HO-FIM improves the service quality of both macro- and femtocell users; in an example simulation scenario, HO-FIM is shown to improve the signal-to-interference and noise ratios (SINRs) of macro- and femtocell users by up to 23.3 and 2.9 dB, respectively, on average, compared to the case of no handovers.

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Dynamic Spectrum Sharing for Hybrid Access in OFDMA-Based Cognitive Femtocell Networks

Cited by 27 publications

References 44 publications

Advances and Emerging Challenges in Cognitive Internet-of-Things

Advances and Emerging Challenges in Cognitive Internet-of-Things

Hybrid Precoding for Massive mmWave MIMO Systems

Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells

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