Linear Space-Time Interference Alignment for $K$ -User MIMO Interference Channels

Jing, Xiaorong; Mo, Linlin; Liu, Hongqing; Zhang, Cuicui

doi:10.1109/access.2017.2787153

Cited by 17 publications

(17 citation statements)

References 30 publications

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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%

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%

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Abdulkadir

Simpson

Sun

2019

JSAN

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“…Obviously, these orthogonal access methods are unable to make full use of valuable communication resources to cope with the high demanding communication requirements nowadays. Therefore, interference alignment (IA) has been proposed for combating interference in an effective way for MIMO X channels [2], [3] and K users MIMO interference channels [4], [5], [6] and cooperative networks [7]. The idea of IA is that by jointly designing precoding and decoding matrices, the interference signals are aligned in a lower subspace at each receiver while the remaining interference-free subspace is reserved for the desired signals.…”

Section: Backgrounds and Motivationmentioning

confidence: 99%

“…where i S is the cardinality of set i S . Based on the results of Corollary 3.4("the divisible case") in [16], for the case of 1 L = , equality (2) has solutions almost surely if and only if (4) is satisfied. The computation of { } , ,…”

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confidence: 99%

Best-Effort Interference Alignment for K Users Quasi-Static MIMO Interference Channels

2019

KSII TIIS

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Interference alignment (IA) has been a powerful approach to achieve the maximum degree of freedom (DoF) for K users multiple-input-multiple-output (MIMO) interference channels. However, due to the feasibility constraint, aligning all the interference signals at each receiver is impractical for large K without symbol extension. In this paper, we propose two best-effort interference alignment (BEIA) schemes that the network selects the maximum number of interfering transmitters to align their signals given the feasibility conditions when each transmitter-receiver pair has a constant number of data streams. Besides, in case of not all interfering signals aligned at each receiver, an upper bound of the average throughput is derived. Simulation results show that the proposed schemes have superiority over the traditional methods, such as time division multiple access (TDMA) and cluster IA(CIA), in low and moderate signal-to-noise ratio (SNR) region in terms of average user throughput. In addition, the proposed max-min relative interference distance alignment scheme outperforms the proposed scheme of equal interfering transmitters number alignment in terms of both average user throughput and minimum user throughput.

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“…Accordingly, maximum degrees of freedom (DoFs) for the whole network can be achieved. The IA scheme is studied for many different networks, e.g., X channel [10], K-user interference channel (IC) [8,11], heterogeneous networks [12][13][14], and cognitive radio networks [15,16]. Moreover, the importance of the channel state information (CSI) for successful IA implementation is addressed in many works [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Evaluating the capacity of a general IC is still a difficult goal for researchers in wireless communications and information theory [19] communities. However, IA technique is introduced as a DoFs optimal approach to interference management [10,11]. This means that it can achieve the capacity of the IC at high SNR value.…”

Section: Introductionmentioning

confidence: 99%

Joint interference alignment and power allocation for NOMA-based multi-user MIMO systems

Rihan

Zhang

2018

J Wireless Com Network

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Interference alignment (IA) and non-orthogonal multiple access (NOMA) are key technologies for achieving the capacity scaling required by next generation networks to overcome the unprecedented growth of data network traffic. Each of these technologies was proved to present excellent performance for MIMO systems. In this article, we propose a joint IA and power allocation (PA) framework for NOMA-based multiuser MIMO (MU-MIMO) systems. Different approaches for applying IA in downlink NOMA-based MU-MIMO systems will be addressed while implementing a PA technique that fully exploits the characteristics of NOMA-based systems. The proposed framework aims to maximize the sum-rate of the NOMA-based MU-MIMO system through combining IA with PA. The process begins by initially grouping the system users into clusters for optimum implementation of NOMA. The sum-rate maximization is carried out under cluster power budget, user quality-of-service (QoS), and robust successive interference cancellation (SIC) constraints. Meanwhile, it uses the power domain multiplexing strategy to allow the users within each cluster to share the data streams without exerting interference to one another. Three iterative joint IA and PA algorithms are proposed for NOMA-based MU-MIMO systems. Moreover, these algorithms are compared with orthogonal multiple access (OMA)-based MU-MIMO counterpart as well as the state-of-the-art techniques presented for NOMA-based MU-MIMO systems. Numerical simulations verify that the proposed framework can greatly improve the performance of NOMA-based MU-MIMO systems in terms of the achievable sum-rate when compared with OMA-based MU-MIMO and the state-of-the-art NOMA-based MU-MIMO systems.

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Linear Space-Time Interference Alignment for $K$ -User MIMO Interference Channels

Cited by 17 publications

References 30 publications

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Best-Effort Interference Alignment for K Users Quasi-Static MIMO Interference Channels

Joint interference alignment and power allocation for NOMA-based multi-user MIMO systems

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