Cognitive interference alignment between small cells and a macrocell

Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/ictel.2012.6221210

Cited by 20 publications

(21 citation statements)

References 18 publications

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“…In the context of coexistence of macrocell and the small cells, authors in [11] have applied the IA technique in order to mitigate the interference from small cells towards the macrocell BS. Similarly, the authors in [40] proposed Vandermonde-subspace frequency division multiplexing for the downlink in order to null out the interference of small cells towards primary macro users.…”

Section: Ia In Cognitive Radio Networkmentioning

confidence: 99%

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Cognitive Interference Alignment for Spectral Coexistence

Sharma

Chatzinotas

Ottersten

2014

Cognitive Radio and Networking for Heterogeneous Wireless Networks

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Interference Alignment (IA) has been widely recognized as a promising interference mitigation technique since it can achieve the optimal degrees of freedom in certain interference limited channels. In the context of Cognitive Radio (CR) networks, this technique allows the coexistence of two heterogeneous wireless systems in an underlay cognitive mode. The main concept behind this technique is the alignment of the interference on a signal subspace in such a way that it can be filtered out at the non-intended receiver by sacrificing some signal dimensions. This chapter starts with an overview of IA principle, Degree of Freedom (DoF) concept, and the classification of existing IA techniques. Furthermore, this chapter includes a discussion about IA applications in CR networks. Moreover, a generic system model is presented for allowing the coexistence of two heterogeneous networks using IA approach while relevant precoding and filtering processes are described. In addition, two important practical applications of the IA technique are presented along with the numerical results for underlay spectral coexistence of (i) femtocell-macrocell systems, and (ii) monobeam-multibeam satellite systems. More specifically, an uplink IA scheme is investigated in order to mitigate the interference of femtocell User Terminals (UTs) towards the macrocell Base Station (BS) in the spatial domain and the interference of multibeam satellite terminals towards the monobeam satellite in the frequency domain.

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Section: Ia In Cognitive Radio Networkmentioning

confidence: 99%

“…The performance of three different IA approaches mentioned in Sect. 2.4 have been compared with the resource division and no-mitigation techniques in [11,57]. Based on the simulation parameters and environment considered in [57], Fig.…”

Section: Macrocell-femtocell Coexistence In Spatial Domainmentioning

confidence: 99%

Cognitive Interference Alignment for Spectral Coexistence

Sharma

Chatzinotas

Ottersten

2014

Cognitive Radio and Networking for Heterogeneous Wireless Networks

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“…IA-based CR can be applied to applications considering interference between heterogeneous systems. Chatzinotas and Ottersten applied IA to small cells to mitigate interference in macrocell base stations in small cells [19], and Huang et al investigated a joint opportunistic interference avoidance scheme using the interweave paradigm-based Gale-Shapley spectral-sharing scheme to mitigate interference between a macrocell network and a femtocell network [20]. Sharma et al proposed a coordinated and uncoordinated approach in a system consisting of monobeam and multibeam satellites as well as macrocell and small-cell systems [21].…”

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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“…This system model has been already considered in [4], [5] focusing on physical layer interference mitigation. More specifically, a cognitive interference alignment has been proposed for enabling the primary-secondary coexistence by exploiting multiuser MIMO schemes.…”

Section: Introductionmentioning

confidence: 99%

Secondary User Scheduling under Throughput Guarantees for the Primary Network

Kapetanović

Butt

Chatzinotas

et al. 2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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Abstract-This work addresses scheduling in a cognitive radio scenario where a minimum throughput for the downlink primary network (PN) is guaranteed to each user with an associated violation probability (probability of not obtaining the guaranteed throughput). The primary network is surrounded by multiple downlink secondary networks, each aiming to maximize its network throughput. Scheduling in PN is performed independent of the secondary networks. Some information about the PN is available at the central scheduler that is responsible for scheduling the secondary networks. The contribution of this work is to apply a novel scheduler to the PN which is more robust to QoS degradations resulting from the secondary networks than other state of the art schedulers. This is validated by numerical simulations of the cognitive radio network.

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Cognitive interference alignment between small cells and a macrocell

Cited by 20 publications

References 18 publications

Cognitive Interference Alignment for Spectral Coexistence

Cognitive Interference Alignment for Spectral Coexistence

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

Secondary User Scheduling under Throughput Guarantees for the Primary Network

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