WNCG - Wireless Networking and Communications Group - Path Loss Models
https://wncg.org/tags/path-loss-models
enResolution-Adaptive Hybrid MIMO Architectures for mmWave Communication
https://wncg.org/research/briefs/resolution-adaptive-hybrid-mimo-architectures-mmwave-communication
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Friday, January 12, 2018</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>Moving to a millimeter wave (mmWave) spectrum in range of 30-300 GHz enables the utilization of multi-gigahertz bandwidth and offers an order of magnitude increase in achievable rate. The small wavelength allows a large number of antennas to be packed into transceivers with very small antenna spacing. Leveraging the large antenna arrays, mmWave systems can manipulate directional beamforming to produce high beamforming gain, which helps overcome large free-space pathloss of mmWave signals.</p>
<p>Problems with hardware cost and power consumption, however, arise from deploying large antenna arrays coupled with power-demanding ADCs. To overcome these challenges, hybrid analog-digital processing architectures that attempt to reduce the burden of fully digital processing, and receivers with low-resolution ADCs have attracted the most interest in recent years.</p>
<p>To take advantage of the two considered architectures, WNCG Professor Brian L. Evans, WNCG student Mr. Jinseok Choi, and Huawei Baseband SOC CTO Dr. Alan Gatherer propose a hybrid massive-MIMO architecture with resolution-adaptive ADCs for mmWave communications. They investigate the ADC bit-allocation problem to minimize the quantization distortion of received signals by leveraging the flexibility of ADC resolutions. A derived closed-form bit-allocation solution reveals that the optimal number of ADC bits increases logarithmically proportional to the RF chain's SNR raised to the 1/3 power given the ADC power constraint. The proposed algorithm that utilizes the solution outperform the conventional fixed ADCs in terms of both the sum rate and energy efficiency in the mmWave communication environment.</p>
<p>This research was supported by Huawei Technologies.</p>
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<p><strong>References</strong></p>
<p><a href="https://sites.google.com/site/jinseokchoi89/">J. Choi</a>, <a href="http://users.ece.utexas.edu/~bevans">B. L. Evans</a> and <a href="https://www.linkedin.com/in/alangatherer/">A. Gatherer</a>, <a href="http://www.ece.utexas.edu/~bevans/papers/2018/mmWaveReceiver/index.html">``Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications''</a>, <em>IEEE Transactions on Signal Processing</em>, vol. 65, no. 23, pp. 6201-6216, Dec. 2017, DOI 10.1109/TSP.2017.2745440.</p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/brian-evans">Brian Evans</a></div></div></div><div class="field field-name-field-related-students field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Researchers: </div><div class="field-items"><div class="field-item even"><a href="/people/students/jinseok-choi">Jinseok Choi</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/mimo">MIMO</a>, <a href="/tags/mmwave">mmWave</a>, <a href="/tags/path-loss-models">Path Loss Models</a>, <a href="/tags/wncg-research">WNCG research</a>, <a href="/tags/huawei">Huawei</a></div></div></div>Fri, 12 Jan 2018 17:13:21 +0000jlu754127 at https://wncg.orghttps://wncg.org/research/briefs/resolution-adaptive-hybrid-mimo-architectures-mmwave-communication#commentsUnderstanding Ultra-Dense Cellular Networks: Multi-slope Path Loss Models and Analysis
https://wncg.org/research/briefs/understanding-ultra-dense-cellular-networks-multi-slope-path-loss-models-and
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Thursday, January 22, 2015</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>Existing cellular network analyses, and even simulations, typically use the standard path loss model where received power decays <em>1/d^x</em> over a distance <em>d</em>, with a pathloss exponent <em>x</em>. This model leads to tractable analysis of downlink cellular network performance with base stations distributed by a Poisson point process. However, it is widely known that this standard path loss model is quite idealized, and that in most scenarios the path loss exponent <em>x</em> is itself a function of <em>d</em>. This is particularly important as networks become increasingly dense, since the path loss exponents governing many nearby transmissions (and their interference) may be small, even less than two in some cases. Such low path loss has large implications on the gain from network densification, including in millimeter wave spectrum.</p>
<p>In this work, WNCG Postdoctoral Associate, Xinchen Zhang, and Prof. Jeffrey Andrews introduced some novel analytical techniques for multi-slope path loss models, where different distance ranges are subject to different path loss exponents. The team focused on the dual-slope path loss function and deriving the coverage probability (relative to an SINR or SIR target) and potential throughput. The exact mathematical results show that the SIR monotonically decreases with network density and (consequently) network coverage is maximized at some finite density. The WNCG researchers also observed some surprising asymptotic results in the event of ultra-densification (BS density approaches infinity). For example, in some instances the coverage probability and/or the rate approach zero for each node.</p>
<p>For more information, read the full paper <a href="http://arxiv.org/abs/1408.0549">HERE</a>. </p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/jeffrey-andrews">Jeffrey Andrews</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/wncg-research">WNCG research</a>, <a href="/tags/ultra-dense-cellular-networks">Ultra-Dense Cellular Networks</a>, <a href="/tags/path-loss-models">Path Loss Models</a>, <a href="/tags/cellular-networks">Cellular Networks</a></div></div></div>Thu, 22 Jan 2015 16:39:11 +0000lab27993610 at https://wncg.orghttps://wncg.org/research/briefs/understanding-ultra-dense-cellular-networks-multi-slope-path-loss-models-and#comments