Thompson Sampling-Based Antenna Selection With Partial CSI for TDD Massive MIMO Systems

Kuai, Zhenran

doi:10.1109/tcomm.2020.3024199

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…AS under complete CSI has been extensively investigated by researchers with the objective of designing a near-optimal algorithm with low computational complexities for massive MIMO [6]- [14]. However, it should be noted that most existing works were established in the presence of complete CSI, which implies that the channel coefficients of all antennas must be fully observed [15]- [17].…”

Section: A Antenna Selection In Massive Mimomentioning

confidence: 99%

“…Since the number of RF chains is smaller than the number of antennas, only an incomplete CSI can be observed and estimated from each pilot transmission. This indicates that acquiring complete CSI eventually leads to extra pilot transmissions and reducing the effective transmission rate [1], [15], [17], [18]. For instance, acquiring complete CSI could occupy more than half of the frame duration for the downlink of a typical time-division duplexing (TDD) massive MIMO system with 64 antennas, 16 RF chains and 4 singleantenna users [15], [19].…”

Section: A Antenna Selection In Massive Mimomentioning

confidence: 99%

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Risk-Aware Antenna Selection for Multiuser Massive MIMO Under Incomplete CSI

He,

Vu,

Hoang

et al. 2024

IEEE Trans. Wireless Commun.

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This paper investigates the antenna selection problem in massive multiple-input multiple-out (MIMO) systems under incomplete channel state information (CSI), with a particular interest on risk-aware planning subjected to practical constraints such as transmit power budgets and quality of services (QoS). Due to a very large number of antennas, obtaining complete channel measurements becomes a cost-prohibitive, energy-inefficient and spectral-inefficient task. To reduce pilot overhead, incomplete CSI and antenna selection (AS) are expected in practical massive MIMO systems. However, most existing AS algorithms heavily rely on the complete CSI, which imposes a high probability of violating the practical constraints in the scenarios of our interests. Motivated by this, we propose a joint channel prediction and antenna selection framework (JCPAS) which efficiently performs AS and is robust against the incomplete CSI and practical constraints. The proposed framework comprises i) a channel tracker which estimates the channel dynamics based on historical incomplete observations, and ii) a risk-aware Monte Carlo tree search (RA-MCTS) algorithm which utilizes the estimated channel dynamics to select antennas in a risk-aware manner. Simulation results show that the proposed RA-MCTS not only achieves much lower energy consumption compared to the existing typical algorithms, but also significantly reduces the probability of violating the practical constraints.Index Terms-Massive MIMO, antenna selection, incomplete CSI, machine learning, risk-aware planning, Monte Carlo tree search I. INTRODUCTIONM ASSIVE multiple-input multiple-out (MIMO) has became the key technology to support the continuous development of future wireless network applications [2]- [5]. By deploying a very large number of antennas at the base station (BS), massive MIMO is capable of significantly improving the spectral efficiency via spatial multiplexing gain Manuscript

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Section: A Antenna Selection In Massive Mimomentioning

confidence: 99%

Section: A Antenna Selection In Massive Mimomentioning

confidence: 99%

Risk-Aware Antenna Selection for Multiuser Massive MIMO Under Incomplete CSI

He,

Vu,

Hoang

et al. 2024

IEEE Trans. Wireless Commun.

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“…• OAS [14]: The online antenna selection (OAS) algorithm introduced in [14]. Instead of exploiting the history, this algorithm obtains partial CSI by modeling the problem as a MAB problem.…”

Section: B Competing Algorithmsmentioning

confidence: 99%

“…Nevertheless, the prediction methods in [11]- [13] require the fully observed channels history and are not applicable to the case of partial observation, especially when the number of antennas exceeds the number of RF chains. Recently, AS with partial CSI has been proposed in [14] based on multi-armed bandit (MAB) and Thompson sampling technique to reduce the channel estimation overhead. This approach, as we will show later, performs poorly when the number of RF chains is significantly smaller than the number of antennas, since the history of partial observations is not exploited.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Joint Channel Prediction and Antenna Selection for Massive MIMO with Partial CSI

Chatzinotas

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels.

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“…Therefore, the proposed scheme efficiently enhanced BER and throughput performance compared to the conventional MIMO-NOMA system and thus developed IoT system. Also, machine learning model for selecting antenna will be considered for the future work in 5G or beyond wireless communication system [27].…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Subcarrier BD with Cooperative Communication for MIMO-NOMA System

Baik¹,

Kim²,

Shin³

et al. 2022

Computers, Materials &Amp; Continua

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With the rapid evolution of Internet of things (IoT), many edge devices require simultaneous connection in 5G communication era. To afford massive data of IoT devices, multiple input multiple output non-orthogonal multiple access (MIMO-NOMA) method has been considered as a promising technology. However, there are numerous drawbacks due to error propagation and inter-user interferences. Therefore, proposed scheme aims to improve the reliability of the MIMO-NOMA system with digital beamforming and intracluster cooperative multi point (CoMP) to efficiently support IoT system. In the conventional MIMO-NOMA system, user entities are grouped into clusters. Block diagonalization (BD) is applied to efficiently eliminate the inter-cluster interference of the MIMO-NOMA system. However, since the channel path of the data stream from a single antenna to a single cluster doesn't hold other cluster's data, the system can't fully utilize the selective subcarrier channel states. It indicates that there can be better channel paths for a data stream at a certain subcarrier index. Therefore, proposed scheme allocates data streams to antennas adaptively considering selective channel states. Additionally, intra-cluster CoMP method is adjusted to enhance the reliability of the system in the clusters. The simulation results show that the proposed scheme improves BER and throughput performance compared to the conventional MIMO-NOMA system.

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Thompson Sampling-Based Antenna Selection With Partial CSI for TDD Massive MIMO Systems

Cited by 21 publications

References 30 publications

Risk-Aware Antenna Selection for Multiuser Massive MIMO Under Incomplete CSI

Risk-Aware Antenna Selection for Multiuser Massive MIMO Under Incomplete CSI

Learning-Based Joint Channel Prediction and Antenna Selection for Massive MIMO with Partial CSI

Subcarrier BD with Cooperative Communication for MIMO-NOMA System

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