Analytical Calculation of Rician K-Factor for Indoor Wireless Channel Models

Mukherjee, S. K.; Das, Suvra Sekhar; Chatterjee, Aritra; Chatterjee, Sourav

doi:10.1109/access.2017.2750722

Cited by 22 publications

(12 citation statements)

References 24 publications

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“…9 demonstrates the robustness of hieP M to the Rician AR-1 fading channel model described above with coherence time T c = 2 ms of the AR process β t , and a Rician factor K r = 10 (this is a reasonable value, e.g. indoor mmWave channel models [27]). We again use an erroneous/mismatched and fixed estimate of the fading coefficientα t =α 0 ∼ CN (α 0 , σ 2 α ) for t = 1, 2, .…”

Section: Time Varying Channelmentioning

confidence: 92%

Active Learning and CSI Acquisition for mmWave Initial Alignment

Chiu

Ronquillo

Javidi

2019

IEEE J. Select. Areas Commun.

108

133

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Millimeter wave (mmWave) communication with large antenna arrays is a promising technique to enable extremely high data rates due to large available bandwidth in mmWave frequency bands. In addition, given the knowledge of an optimal directional beamforming vector, large antenna arrays have been shown to overcome both the severe signal attenuation in mmWave as well as the interference problem. However, fundamental limits on achievable learning rate of an optimal beamforming vector remain.This paper considers the problem of adaptive and sequential optimization of the beamforming vectors during the initial access phase of communication. With a single-path channel model, the problem is reduced to actively learning the Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station (BS). Drawing on the recent results in the design of a hierarchical beamforming codebook [1], sequential measurement dependent noisy search strategies [2], and active learning from an imperfect labeler [3], an adaptive and sequential alignment algorithm is proposed.For any given resolution and error probability of the estimated AoA, an upper bound on the expected search time of the proposed algorithm is derived via Extrinsic Jensen-Shannon Divergence. The upper bound demonstrates that the search time of the proposed algorithm asymptotically matches the performance of the noiseless bisection search up to a constant factor, in effect, characterizing the AoA acquisition rate. Furthermore, the upper bound shows that the acquired AoA error probability decays exponentially fast with the search time with an exponent that is a decreasing function of the acquisition rate.Numerically, the proposed algorithm is compared with prior work where a significant improvement of the system communication rate is observed. Most notably, in the relevant regime of low (−10dB to +5dB) raw SNR, this establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication.

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Section: Time Varying Channelmentioning

confidence: 92%

Active Learning and CSI Acquisition for mmWave Initial Alignment

Chiu

Ronquillo

Javidi

2019

IEEE J. Select. Areas Commun.

108

133

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“…The Rician KF statistics is a measure of the ratio of LOS‐to‐NLOS strength and its value affects the performance of MIMO systems significantly [32]. The presence of LOS component translates to stronger propagation and larger signal‐to‐noise ratio [22].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To compare the performance of DSRC and LTE‐A with the mmWave massive MIMO advocated in this work, we characterise the vehicular channel using PL, root‐mean‐square delay spread (

τ_{RMS}

), Rician K ‐factor (KF), number of clusters, number of resolvable MPCs/rays/subpaths, power delay profile (PDP), channel rank, channel condition number and data rate ( R ). For the analysis, PL is evaluated using (3) in Section 2.2 while the RMS delay spread

τ_{RMS}

is evaluated using the following equation [31]:

τ_{RMS} = \sqrt{\frac{{false\sum}_{c = 1}^{N_{cl}} \sum_{s = 1}^{N_{s p, c}} τ_{c, s}^{2} P_{RX, c, s}}{{false\sum}_{c = 1}^{N_{cl}} \sum_{s = 1}^{N_{s p, c}} P_{RX, c, s}} - {()(, \frac{\sum_{c = 1}^{N_{cl}} {false\sum}_{s = 1}^{N_{sp, c}} τ_{c, s} P_{RX, c, s}}{\sum_{c = 1}^{N_{cl}} {false\sum}_{s = 1}^{N_{sp, c}} P_{RX, c, s}})}^{2}}

The Rician KF is evaluated using the following equation [32]:

KF = \frac{P_{RX, c = 1, s = 1}}{()(, \sum_{c = 1}^{N_{cl}} {false\sum}_{s = 1}^{N_{s}})}

…”

Section: Performance Metricsmentioning

confidence: 99%

Millimetre‐wave massive MIMO for cellular vehicle‐to‐infrastructure communication

Busari

Khan

Huq

et al. 2019

IET Intelligent Transport Systems

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“…The correlation parameter g is set such that coherence time T c = 2 ms (equivalent to τ = 28 total slots using the 5G NR PRACH format B4 [15]), and a Rician factor k r = 10 (this is a reasonable value, e.g. indoor mmWave channel models [16]). For these parameters, we assume a conservative range for α t to be: [r min , r max ] = [0, 2], [z min , z max ] = [−0.7, 0.7], with resolution parameters 1 r = 1 z = 50 3 .…”

Section: A Simulation Scenariomentioning

confidence: 99%

Sequential Learning of CSI for MmWave Initial Alignment

Ronquillo

Chiu

Javidi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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MmWave communications aim to meet the demand for higher data rates by using highly directional beams with access to larger bandwidth. An inherent challenge is acquiring channel state information (CSI) necessary for mmWave transmission. We consider the problem of adaptive and sequential learning of the CSI during the mmWave initial alignment phase of communication. We focus on the single-user with a single dominant path scenario where the problem is equivalent to acquiring an optimal beamforming vector, where ideally, the resulting beams point in the direction of the angle of arrival with the desired resolution. We extend our prior by proposing two algorithms for adaptively and sequentially selecting beamforming vectors for learning of the CSI, and that formulate a Bayesian update to account for the time-varying fading model. Numerically, we analyze the outage probability and expected spectral efficiency of our proposed algorithms and demonstrate improvements over strategies that utilize a practical hierarchical codebook.

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Analytical Calculation of Rician K-Factor for Indoor Wireless Channel Models

Cited by 22 publications

References 24 publications

Active Learning and CSI Acquisition for mmWave Initial Alignment

Active Learning and CSI Acquisition for mmWave Initial Alignment

Millimetre‐wave massive MIMO for cellular vehicle‐to‐infrastructure communication

Sequential Learning of CSI for MmWave Initial Alignment

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