Channel estimation and user selection in the MIMO broadcast channel

Davis, Linda M.; Hanly, Stephen V.; Tune, Paul; Bhaskaran, S.R.

doi:10.1016/j.dsp.2011.01.003

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…Thus, the length of τ k can be short. As in (21), one symbol can be sufficient. This can reduce the training overhead, which might be crucial when the coherence time is limited.…”

Section: A Sparse Representation and Cs-based Approachmentioning

confidence: 98%

“…CS is a powerful tool and can be applied to a number of problems from image compression to radar applications [18]. In wireless communications, CS is also applied to various sparse multipath channel estimation problems [19]- [21] and CSI feedback [22], [23]. For the mm-wave channel estimation under a certain sparsity (or limited scattering) environment, a CS-based approach is used in [14], [24].…”

Section: Introductionmentioning

confidence: 99%

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Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing

Choi

2015

IEEE Trans. Commun.

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In this paper, we study beam selection for millimeterwave (mm-wave) multiuser multiple input multiple output (MIMO) systems where a base station (BS) and users are equipped with antenna arrays. Exploiting a certain sparsity of mm-wave channels, a low-complexity beam selection method for beamforming by low-cost analog beamformers is derived. It is shown that beam selection can be carried out without explicit channel estimation using the notion of compressive sensing (CS). Due to various reasons (e.g., the background noise and interference), some users may choose the same BS beam, which results in high inter-user interference. To overcome this problem, we further consider BS beam selection by users. Through simulations, we show that the performance gap between the proposed approach and the optimal beamforming approach, which requires full channel state information (CSI), becomes narrower for a larger number of users at a moderate/low signal-to-noise ratio (SNR). Since the optimal beamforming approach is difficult to be used due to prohibitively high computational complexity for large antenna arrays with a large number of users, the proposed approach becomes attractive for BSs and users in mm-wave systems where large antenna arrays can be employed.

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“…Thus, the length of τ k can be short. As in (21), one symbol can be sufficient. This can reduce the training overhead, which might be crucial when the coherence time is limited.…”

Section: A Sparse Representation and Cs-based Approachmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing

Choi

2015

IEEE Trans. Commun.

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“…In our distributed scheduling, each user i ∈ B compares its channel gain |h i | 2 with a certain threshold value τ and transmits only if the channel gain is greater than the threshold. The idea of self-scheduling exploiting the threshold has been introduced in [7], [8]. We define the set of scheduled users…”

Section: Sparsity Controlled Random Multiple Access With Csitmentioning

confidence: 99%

Sparsity Controlled Random Multiple Access With Compressed Sensing

Hong

Choi

Rao

2015

IEEE Trans. Wireless Commun.

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The paper considers random multiple access in a network where only a small portion of users have data to forward and transmit packets in each time slot because user activity ratio is not high in practice. For this reason, the AP has to not only identify the users who transmitted but also decode the received data codewords. Exploiting the sparsity of transmitting users, Lasso, a well-known practical compressed sensing algorithm, is applied for efficient user identification. The compressed sensing algorithm enables the AP to handle more users than the conventional random multiple access schemes do. We develop distributed scheduling methods for maximizing system sum throughput and analyze the corresponding optimal throughput for three different cases of channel knowledgechannel state information at transmitter (CSIT), channel state information at receiver (CSIR), and imperfect channel state information at receiver (ImCSIR). We also derive closed form expressions of asymptotically optimal scheduling parameters and the corresponding maximum sum throughput for each CSI assumption. The results show the effects of system parameters on sum throughput, and it provides useful insights on using compressed sensing for throughput maximization in random multiple access schemes.

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“…where (0, 2]   is the characteristic factor,      is the location parameter, [ 1,1]   is the symmetry parameter, and 0   is the dispersion parameter. The characteristic factor Fig.4.…”

Section: Time-varying Channel Estimation Under Mixed Gaussian Noisementioning

confidence: 99%

Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments

Gui

et al. 2015

Journal of the Franklin Institute

204

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Abstract:Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS),which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.

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Channel estimation and user selection in the MIMO broadcast channel

Cited by 19 publications

References 18 publications

Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing

Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing

Sparsity Controlled Random Multiple Access With Compressed Sensing

Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments

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