Resolution-Adaptive Hybrid MIMO Architectures for mmWave Communication

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.

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.

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.

This research was supported by Huawei Technologies.

J. Choi, B. L. Evans and A. Gatherer, "Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications", IEEE Transactions on Signal Processing, vol. 65, no. 23, pp. 6201-6216, Dec. 2017, DOI 10.1109/TSP.2017.2745440.

Wideband Channel Estimation for Hybrid Beamforming Millimeter Wave Communication Systems with Low-Resolution ADCs

Millimeter wave (mmWave) communications is a promising technology to increase data rates to users. In mmWave communications, large antenna arrays are needed to form directional beams to overcome the high pathloss. Power consumption becomes a significant challenge. By reducing the number of RF chains and adapting the analog-to-digital converter (ADC) resolution in each RF chain, power consumption can be dramatically lowered.

One receiver architecture to achieve both power reduction goals uses analog combining and digital beamforming. The output of the analog combiner is sparse. That allows the number of RF chains and the number of ADC bits to be tailored to teach beam. Some beams have such low received signal-to-noise ratio that no RF chain would need to applied to it.

In this hybrid beamforming architecture with low-resolution ADCs, conventional channel estimation algorithms such as least squares and minimum mean square error estimation not perform well because they do not exploit the sparsity in the channel impulse responses.

For wideband channel estimation in mmWave systems, WNCG Professor Brian L. Evans and students Mr. Junmo Sung and Mr. Jinseok Choi have evaluated compressed sensing algorithms such as orthogonal matching pursuit (OMP) and generalized approximate message passing (GAMP) as well as the least squares (LS) estimator for comparison purposes. Through simulation, they have shown that one-bit ADCs using OMP outperform infinite-bit ADCs using LS in an SNR range of interest. Using OMP, low-resolution such as 3 and 4 bits can achieve channel estimation performance close to the infinite bit resolution case across the SNR range of -10 dB to 15 dB.

This research was supported by Huawei Technologies.

Machine Learning to Improve Success Rates for Handovers from Sub-6 GHz LTE to Millimeter Wave Bands

Transmission over millimeter wave (mmWave) frequency bands is being adopted in fifth generation (5G) wireless communications. Even though the sub-6 GHz frequency bands continue to dominate deployments due to their better ability to penetrate and provide in-building coverage, the handover between mmWave and sub-6 GHz frequency bands is nonetheless inevitable to support higher data rates. The cost of a handover is a reduction in data rate, which 5G promises to increase.

While it is always possible for handset manufacturers to introduce new receiver circuitry for mmWave bands in 5G, the price of such a handset will be more expensive and the battery will deplete faster when having to measure two bands simultaneously.

WNCG Professor Brian L. Evans and PhD student Mr. Faris Mismar have introduced a new category of partially blind algorithms for inter-radio access technology handover between LTE-Advanced and 5G. The handover is partially blind because it makes a decision to handover to mmWave bands based on the current sub-6 GHz LTE-Advanced channel measurements. The decision comes from the probability of handover success from an extreme gradient boosting classifier that has been trained using simultaneous LTE-Advanced nd mmWave channel measurements. If the classifier finds that the handover to mmWave is likely to succeed, it will allow the handover measurements to start by allowing the LTE-Advanced base station to configure an occasion for the handset to measure the mmWave channel. This occasion is known as the measurement gap. If not, the base station instructs the handset to remain in the LTE-Advanced service area. This way, the measurement gap is not opened, and the handset continues to receive scheduled data from the better performing technology.

F. B. Mismar and B. L. Evans, “Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling”, Proc. IEEE International Conference on Communications Workshop on Evolutional Tech. & Ecosystems for 5G Phase II, May 20-24, 2018, Kansas City, MO, USA.

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