Achievable Sum Rate and Degrees of Freedom of Opportunistic Interference Alignment in MIMO Interfering Broadcast Channels

Suo, Long; Li, Jiandong; Li, Hongyan; Zhang, Shun; Davidson, T.N.

doi:10.1109/tcomm.2019.2903250

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…According to the integral and limit theories, a generalized method was presented to discuss the sum‐rate in [22], in which the sum‐rate was considered as a series in regard to the negative power of the transmitting antennas. According to the analysis of the sum‐rate, an effective performance evaluation method was proposed for the multi‐cell opportunistic interference alignment in [23]. In [24], a low complexity alternating optimization algorithm was proposed to improve the weighted sum‐rate, which also considered the distortion in the receiver and transmitter.…”

Section: Related Workmentioning

confidence: 99%

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Xie¹,

Ai²

2021

IET Communications

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Sum‐rate optimization problem is a key issue in a time‐varying multi‐user multi‐input multi‐output (MU‐MIMO) distributed antenna system. Channel precoding matrix is the key technology in the achievable sum‐rate optimization problem. In this paper, two algorithms are proposed to solve the achievable sum‐rate optimization problem for a time‐varying MU‐MIMO distributed antenna system, namely one‐dimensional search algorithm (OSA) and cyclic MMSE (Minimum Mean Square Error) search algorithm (CMSA). In order to design the channel precoding matrix for the achievable sum‐rate optimization, a one‐dimensional search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. For the problem with a large amount of computation in OSA, a cyclic MMSE search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. Simulation results show that the proposed algorithms can effectively improve the sum‐rate compared to other algorithms. Simulation results also show that OSA is superior to CMSA in the sum‐rate, and the channel state information (CSI) coefficient has little effect on OSA and CMSA.

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Section: Related Workmentioning

confidence: 99%

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Xie¹,

Ai²

2021

IET Communications

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“…To let the technique of IA make more practical sense, opportunistic interference alignment (OIA) has been developed for the networks with multiple cells or clusters in both downlink [23][24][25][26] and uplink [14,15,27,28] transmissions. In this regard, OIA has many advantages over the traditional IA schemes [29,30].…”

Section: Related Workmentioning

confidence: 99%

“…In [25], a joint transmit/receive beamforming and user scheduling scheme is designed, which fundamentally reduces the user scaling law as shown in [23], and a limited feedback strategy is analyzed. Additionally, for the OIA strategy, the performance of achievable sum rate is analyzed in MIMO interfering broadcast channels in [26].…”

Section: Related Workmentioning

confidence: 99%

Interference Alignment Inspired Opportunistic Communications in Multi-Cluster MIMO Networks with Wireless Power Transfer

2021

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In this work, we jointly investigate the issues of node scheduling and transceiver design in a sensor network with multiple clusters, which is endowed with simultaneous wireless information and power transfer. In each cluster of the observed network, S out of N nodes are picked, each of which is capable of performing information transmission (IT) via uplink communications. As for the remaining idle nodes, they can harvest energy from radio-frequency signals around their ambient wireless environments. Aiming to boost the intra-cluster performance, we advocate an interference alignment enabled opportunistic communication (IAOC) scheme. This scheme can yield better tradeoffs between IT and wireless power transfer (WPT). With the aid of IAOC scheme, the signal projected onto the direction of the receive combining vector is adopted as the accurate measurement of effective signal strength, and then the high-efficiency scheduling metric for each node can be accordingly obtained. Additionally, an algorithm, based on alternative optimization and dedicated for transceiver design, is also put forward, which is able to promote the achievable sum rate performance as well as the total harvested power. Our simulation results verify the effectiveness of the designed IAOC scheme in terms of improving the performance of IT and WPT in multi-cluster scenarios.

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“…Asymptotic IA is an opportunistic technique that exploits the presence of complementary channel states in equal magnitudes to achieve linear IA [28]. [33][34][35][36] Distributed IA Local Single [37][38][39][40] Subspace IA Perfect Multi [29,[41][42][43] Blind IA Absent Single [25,44,45] Ergodic IA Perfect/delayed Single [36,46,47] Retrospective IA Delayed Single [26,39,43] Lattice Alignment Perfect Single [40,[48][49][50][51] IA and Cancelation Perfect Single/Multi [27,52,53] Opportunistic IA Perfect Single/Multi [28,54,55] Asymptotic IA Perfect Single…”

Section: General Ia Techniquesmentioning

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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Achievable Sum Rate and Degrees of Freedom of Opportunistic Interference Alignment in MIMO Interfering Broadcast Channels

Cited by 15 publications

References 20 publications

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Interference Alignment Inspired Opportunistic Communications in Multi-Cluster MIMO Networks with Wireless Power Transfer

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

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