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1. Effects of Beam Misalignment on Heterogeneous Cellular Networks with Mm-Wave Small Cells

   https://doi.org/10.1109/GLOBECOM48099.2022.10001400  

   Zia, Muhammad Saad ; Blough, Douglas M. ; Weitnauer, Mary Ann ( January 2022 , Proceedings of the IEEE Global Communications Conference)

   This paper studies the effects of millimeter-wave (mm-wave) beam alignment errors on the downlink achievable rate of a heterogeneous network (HetNet), which consists of sub-6 GHz macro-cells and mm-wave small-cells. The alignment error is modeled as a function of the underlying mm-wave link parameters. The conventional maximum biased received power criterion, where the bias is used for mm-wave small-cells, is adopted for cell associations. By varying the value of the bias factor, we investigate the changes in the downlink rate coverage probability. Our simulation results indicate that high values (of the order of 30 dB) for the bias, while beneficial in the case of perfect alignment, are actually disadvantageous for the low-rate users in the case of imperfect beam alignment. The low-rate users are better served by a moderate value (of the order of 20 dB) of the bias when the beam alignment errors are accounted for. We also show that the above disparity can be narrowed down by increasing by mm-wave base station (BS) antennas and/or the mm-wave BS density. 

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2. Cell-edge Interferometry: Reliable Detection of Unknown Cell-edge Users via Canonical Correlation Analysis

   https://doi.org/10.1109/SPAWC.2019.8815536  

   Ibrahim, Mohamed Salah ; Sidiropoulos, Nicholas D. ( July 2019 , 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   A key challenge in 4G and emerging 5G systems is that of reliably detecting the uplink transmissions of users close to the edge between cells. These users are subject to significant signal attenuation due to path loss, and frequent hand-off from one cell to the other, making channel estimation very challenging. Even multiuser detection using base station cooperation often fails to detect such users, due to channel estimation errors and the sensitivity of multiuser detection to near-far power imbalance. Is it even possible to reliably decode the cell-edge users' signals under these circumstances? This paper shows, perhaps surprisingly, that with a suitable base station `interferometry' strategy, the cell-edge users' signals can be reliably decoded at low SNR under mild conditions. Exploiting the fact that cell-edge users' signals are weak but common to both base stations, while users close to a base station are unique to that base station, reliable detection is enabled by Canonical Correlation Analysis (CCA) - a machine learning technique that reliably estimates a common subspace, even in the presence of strong individual interference. Free from cell-center interference, the resulting mixture of cell-edge signals can then be unraveled using well-known algebraic signal processing techniques. Simulations demonstrate that the proposed detector achieves order of magnitude BER improvement compared to an `oracle' zero-forcing with successive interference cancellation that assumes perfect knowledge of all channels. The paper also includes proof of common subspace identifiability for the assumed generative model, which was curiously missing from the machine learning / CCA literature. 

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3. Second-Best Beam-Alignment via Bayesian Multi-Armed Bandits

   https://doi.org/10.1109/GLOBECOM38437.2019.9013578  

   Hussain, Muddassar ; Michelusi, Nicolo ( December 2019 , IEEE Global Communications Conference (GLOBECOM))

   Millimeter-wave (mm-wave) systems rely on narrow- beams to cope with the severe signal attenuation in the mm- wave frequency band. However, susceptibility to beam mis- alignment due to mobility or blockage requires the use of beam- alignment schemes, with huge cost in terms of overhead and use of system resources. In this paper, a beam-alignment scheme is proposed based on Bayesian multi-armed bandits, with the goal to maximize the alignment probability and the data-communication throughput. A Bayesian approach is proposed, by considering the state as a posterior distribution over angles of arrival (AoA) and of departure (AoD), given the history of feedback signaling and of beam pairs scanned by the base-station (BS) and the user- end (UE). A simplified sufficient statistic for optimal control is identified, in the form of preference of BS-UE beam pairs. By bounding a value function, the second-best preference policy is formulated, which strikes an optimal balance between exploration and exploitation by selecting the beam pair with the current second-best preference. Through Monte-Carlo simulation with analog beamforming, the superior performance of the second- best preference policy is demonstrated in comparison to existing schemes based on first-best preference, linear Thompson sampling, and upper confidence bounds, with up to 7%, 10% and 30% improvements in alignment probability, respectively. 

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4. High Altitude Platform Stations (HAPS): Architecture and System Performance

   Yunchou Xing, Frank Hsieh ( April 2021 , IEEE Vehicular Technology Conference)

   High Altitude Platform Station (HAPS) has the potential to provide global wireless connectivity and data services such as high-speed wireless backhaul, industrial Internet of things (IoT), and public safety for large areas not served by terrestrial networks. A unified HAPS design is desired to support various use cases and a wide range of requirements. In this paper, we present two architecture designs of the HAPS system: i) repeater based HAPS, and ii) base station based HAPS, which are both viable technical solutions. The energy efficiency is analyzed and compared between the two architectures using consumption factor theory. The system performance of these two architectures is evaluated through Monte Carlo simulations and is characterized in metrics of spectral efficiency using LTE band 1 for both single-cell and multi-cell cases. Both designs can provide good downlink spectral efficiency and coverage, while the uplink coverage is significantly limited by UE transmit power and antenna gain. Using directional antennas at the UEs can improve the system performance for both downlink and uplink. 

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5. Channel Extrapolation in FDD Massive MIMO: Theoretical Analysis and Numerical Validation

   Rottenberg, F. ; Zhang, J. ; Molisch, A. F. ( January 2019 , IEEE Globecom)

   Downlink channel estimation in massive MIMO systems is well known to generate a large overhead in frequency division duplex (FDD) mode as the amount of training generally scales with the number of transmit antennas. Using instead an extrapolation of the channel from the measured uplink estimates to the downlink frequency band completely removes this overhead. In this paper, we investigate the theoretical limits of channel extrapolation in frequency. We highlight the advantage of basing the extrapolation on high-resolution channel estimation. A lower bound (LB) on the mean squared error (MSE) of the extrapolated channel is derived. A simplified LB is also proposed, giving physical intuition on the SNR gain and extrapolation range that can be expected in practice. The validity of the simplified LB relies on the assumption that the paths are well separated. The SNR gain then linearly improves with the number of receive antennas while the extrapolation performance penalty quadratically scales with the ratio of the frequency and the training bandwidth. The theoretical LB is numerically evaluated using a 3GPP channel model and we show that the LB can be reached by practical high-resolution parameter extraction algorithms. Our results show that there are strong limitations on the extrapolation range than can be expected in SISO systems while much more promising results can be obtained in the multiple-antenna setting as the paths can be more easily separated in the delay-angle domain. 

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