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Register a new userBeamwidth Selection for a Uniform Planar Array (UPA) Using RT-ICM mmWave Clusters
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Added by
Yavuz Yaman
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Beamforming is the primary technology to overcome the high path loss in millimeter-wave (mmWave) channels. Hence, performance improvement needs knowledge and control of the spatial domain. In particular, antenna structure and radiation parameters affect the beamforming performance in mmWave communications systems. In order to address the impairments such as beam misalignments, outage loss, tracking inability, blockage, etc., an optimum value of the beamwidth must be determined. In our previous paper, assuming a communication system that creates a beam per cluster, we theoretically investigated the beamwidth-received power relation in the cluster level mmWave channels. We used uniform linear array (ULA) antenna in our analysis. In this paper, we revisit the analysis and update the expressions for the scenario where we use rectangular uniform planar array (R-UPA) antenna. Rectangular beam model is considered to approximate the main lobe pattern of the antenna. For the channel, we derive beamwidth-dependent extracted power expressions for two intra-cluster channel models, IEEE 802.11ad and our previous work based on ray-tracing (RT-ICM). Combining antenna and channel gains, in case of the perfect alignment, we confirm that the optimum beamwidth converges zero. Performing asymptotic analysis of the received power, we give the formulation and insights that the practical nonzero beamwidth values can be achieved although sacrificing subtle from the maximum received power. Our analysis shows that to reach 95% of the maximum power for a typical indoor mmWave cluster, a practical beamwidth of 3.5 deg is enough. Finally, our analysis results show that there is a 13 dB increase in the maximum theoretical received power when UPA is used over ULA. We show that an 8 x 8 UPA can reach 50% of that maximum received power while the received power is still 10 dB larger than the ULA scenario.
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Beamforming for mmWave communications is well-studied in the PHY based on the channel parameters to develop optimum receiver processing techniques. However, even before signal processing, antenna structure and radiation parameters affect the beamforming performance primarily. For example, in contrast to common belief, narrow beamwidth (bmW) may result in degraded beamforming performance. In order to address the impairments such as beam misalignments, outage loss, tracking inability, blockage, etc., an optimum value of the bmW must be determined. In this paper, assuming a communication system that creates a beam per cluster, we theoretically investigate the bmW and received power relation in the cluster level mmWave channels. We adopt ULA structure and formulate its antenna gain with respect to the bmW. Two beam models are considered for the main lobe of the array pattern, rectangular and triangular. We derive bmW-dependent extracted power expressions for two intra-cluster channel models, 802.11ad and our previous work, RT-ICM. Combining antenna and channel gains, in case of a beam misalignment, we find that the optimum bmW that maximizes the received power is larger than the alignment error when the error itself is larger than the standard deviation of the cluster power-angle spectrum. Once the alignment error is smaller than the standard deviation, we confirm that the optimum bmW converges zero. Performing asymptotic analysis of the received power, we give the formulation and insights that the practical nonzero bmW values can be achieved although sacrificing subtle from the maximum received power. Our analysis shows that to reach 95% of the maximum power for an indoor mmWave cluster, a practical bmW of 7-10 degrees is enough, which can be created with 18-20 antenna elements. In the simulation section, we show that the expressions given by the analysis match to the simulated results.
In millimeter-wave (mmWave) channels, to overcome the high path loss, beamforming is required. Hence, the spatial representation of the channel is essential. Further, for accurate beam alignment and minimizing the outages, inter-beam interferences, etc., cluster-level spatial modeling is also necessary. Since, statistical channel models fail to reproduce the intra-cluster parameters due to the site-specific nature of the mmWave channel, in this paper, we propose a ray tracing intra-cluster model (RT-ICM) for mmWave channels. The model considers only the first-order reflection; thereby reducing the computation load while capturing most of the energy in a large number of important cases. The model accounts for diffuse scattering as it contributes significantly to the received power. Finally, since the clusters are spatially well-separated due to the sparsity of first-order reflectors, we generalize the intra-cluster model to the mmWave channel model via replication. Since narrow beamwidth increases the number of single-order clusters, we show that the proposed model suits well to MIMO and massive MIMO applications. We illustrate that the model gives matching results with published measurements made in a classroom at 60 GHz. For this specific implementation, while the maximum cluster angle of arrival (AoA) error is 1 degree, mean angle spread error is 9 degrees. The RMS error for the cluster peak power is found to be 2.2 dB.
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery procedures, contextual information from light detection and ranging (LIDAR) sensors mounted on vehicles has been leveraged by data-driven methods to produce useful side information. In this paper, we propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing, which significantly outperforms previous works. Our solution comprises multiple novelties that improve both the convergence speed and the final accuracy of the model. In particular, we define a novel loss function inspired by the knowledge distillation idea, introduce a curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight (NLOS) information, and we propose a non-local attention module to improve the performance for the more challenging NLOS cases. Simulation results on benchmark datasets show that, utilizing solely LIDAR data and the receiver position, our NN-based beam selection scheme can achieve 79.9% throughput of an exhaustive beam sweeping approach without any beam search overhead and 95% by searching among as few as 6 beams.
The effects of quantization and coding on the estimation quality of a Gauss-Markov, namely Ornstein-Uhlenbeck, process are considered. Samples are acquired from the process, quantized, and then encoded for transmission using either infinite incremental redundancy or fixed redundancy coding schemes. A fixed processing time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the age-of-information, defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such (age-penalty) functional depends on the quantization bits, codeword lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long term average MMSE is minimized. This is then characterized in the setting of general increasing age-penalty functionals, not necessarily corresponding to MMSE, which may be of independent interest in other contexts.
Millimeter-wave (mmWave) networks rely on directional transmissions, in both control plane and data plane, to overcome severe path-loss. Nevertheless, the use of narrow beams complicates the initial cell-search procedure where we lack sufficient information for beamforming. In this paper, we investigate the feasibility of random beamforming for cell-search. We develop a stochastic geometry framework to analyze the performance in terms of failure probability and expected latency of cell-search. Meanwhile, we compare our results with the naive, but heavily used, exhaustive search scheme. Numerical results show that, for a given discovery failure probability, random beamforming can substantially reduce the latency of exhaustive search, especially in dense networks. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave system design.
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