Interference alignment in overlay cognitive radio femtocell networks

Hasani-Baferani, Mohsen; Abouei, Jamshid; Zeinalpour-Yazdi, Zolfa

doi:10.1049/iet-com.2015.0690

Cited by 13 publications

(10 citation statements)

References 33 publications

(53 reference statements)

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“…Applications [56][57][58][59][60] Cognitive Radio Networks [30,41,47,61] K User Interference Channel [27,30,31,46,47] K User M × N MIMO Interference Channels [62,63] 5G Cellular Wireless Networks [64][65][66] Cooperative Interference Networks [66,67] Multihop Interference Networks [68,69] Ad hoc Networks [70,71] Physical Layer Security [72,73] Satellite Networks [74,75] D2D Networks [50,76] IoT Networks [34,35,38,51] Heterogeneous Networks…”

Section: Referencesmentioning

confidence: 99%

“…This MEB scheme has been extended to the work in ref. [69], for femto-cells, which consists of an overlay MIMO cognitive femtocell network with macrocell users equipment (MUEs), taken as the PUs, and femto-cell access points (FAPs), which are taken as the SUs. In this work, the MUEs base station puts all its power on the antenna corresponding to the largest eigenmode of the channel matrix, leaving the rest free.…”

Section: Space-time Water-filling (St-wf)mentioning

confidence: 99%

“…Table 7. Average sum rates against typical SNR values for the second paradigm with space-time water-filling (ST-WF) for the multiple SU MIMO link [69,73,99,127]. Average sum rates against typical SNR values for the second paradigm with SWF for the multiple SU MIMO link [80][81][82][83]93,97,[119][120][121][122][123].…”

Section: Average Sum Rates Of the Su Networkmentioning

confidence: 99%

See 2 more Smart Citations

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Abdulkadir

Simpson

Sun

2019

JSAN

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Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.

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Section: Referencesmentioning

confidence: 99%

Section: Space-time Water-filling (St-wf)mentioning

confidence: 99%

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Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Abdulkadir

Simpson

Sun

2019

JSAN

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“…The basic idea here is to fully exploit the precoding matrix of the cognitive transmitters, then align the cross-tier interference signals into a lower dimensional subspace. More specifically, in each primary receiver, the cognitive interference caused by IoT devices should be aligned into the subspace orthogonal to the primary link so that the desired signal can be recovered free of cross-tier interference [13]. …”

Section: Introductionmentioning

confidence: 99%

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Tian

Wang

et al. 2018

Sensors

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This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.

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“…And Perlaza et al allow the SU to transmit signals without interfering with the PU through the remaining eigenmodes that are not used by the PU [24]. Hasani-Baferani et al enabled the SU to perform IA by providing a femtocell with eigenmode information of the PU in a macrocell and femtocell system [25]. Zhao et al optimized the sum rate of SUs, limiting it to the transmission rate threshold of the PU so that the sum rate of the entire network is optimized according to PU requirements [26].…”

Section: Introductionmentioning

confidence: 99%

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Jang

Yoo

2018

J Wireless Com Network

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In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user's interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs.

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Interference alignment in overlay cognitive radio femtocell networks

Cited by 13 publications

References 33 publications

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

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