Jinseok Choi

Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications

In this paper, we propose a hybrid analog–digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chains signal-to-noise ratio raised to the

ADC bit allocation under a power constraint for mmWave massive MIMO communication receivers

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Space-time fronthaul compression of complex baseband uplink LTE signals

power. Using the solutions, two proposed BA algorithms minimize the mean square quantization error of received analog signals under a total ADC power constraint. Contributions of this paper include 1) ADC bit-allocation algorithms to improve communication performance of a hybrid MIMO receiver, 2) approximation of the capacity with the BA algorithm as a function of channels, and 3) a worst-case analysis of the ergodic rate of the proposed MIMO receiver that quantifies system tradeoffs and serves as the lower bound. Simulation results demonstrate that the BA algorithms outperform a fixed-ADC approach in both spectral and energy efficiency, and validate the capacity and ergodic rate formula. For a power constraint equivalent to that of fixed 4-bit ADCs, the revised BA algorithm makes the quantization error negligible while achieving 22% better energy efficiency. Having negligible quantization error allows existing state-of-the-art digital beamformers to be readily applied to the proposed system.

ANTENNA SELECTION FOR LARGE-SCALE MIMO SYSTEMS WITH LOW-RESOLUTION ADCS

Millimeter wave (mmWave) systems operating over a wide bandwidth and using a large number of antennas impose a heavy burden on power consumption. In a massive multiple-input multiple-output (MIMO) uplink, analog-to-digital converters (ADCs) would be the primary consumer of power in the base station receiver. This paper proposes a bit allocation (BA) method for mmWave multi-user (MU) massive MIMO systems under a power constraint. We apply ADCs to the outputs of an analog phased array for beamspace projection to exploit mmWave channel sparsity. We relax a mean square quantization error (MSQE) minimization problem and map the closed-form solution to non-negative integer bits at each ADC. In link-level simulations, the proposed method gives better communication performance than conventional low-resolution ADCs for the same or less power. Our contribution is a near optimal low-complexity BA method that minimizes total MSQE under a power constraint.

Spectral Efficiency Bounds for Interference-Limited SVD-MIMO Cellular Communication Systems

In this paper, we propose space-time fronthaul compression of baseband uplink LTE signals for cellular networks, in which baseband units (BBUs) support remote radio heads (RRHs) through fronthaul links. In particular, we assume massive antenna arrays in which the number of antennas in a RRH is much larger than the number of active users. The proposed method consists of two phases: dimensionality reduction phase and individual quantization phase. The key idea of the first phase is to apply principal component analysis (PCA). It performs low-rank approximation of a matrix - composed of received signals - by exploiting the correlation of the received signals across space and time. In the second phase, our method individually quantizes the dimensionality-reduced signal by applying transform coding with bit allocation to reduce the number of quantization bits. An LTE link-level simulator provides numerical results which show that the method achieves up to 8 × compression ratio for the uplink with 64 antennas and 4 active users, along with improvement in communication system performance as a result of denoising.

Narrowband Channel Estimation for Hybrid Beamforming Millimeter Wave Communication Systems with One-Bit Quantization

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

User Scheduling for Millimeter Wave MIMO Communications with Low-Resolution ADCs

The ergodic spectral efficiency (SE) in interference-limited multiple-input multiple-output (MIMO) downlink cellular systems is characterized based on stochastic geometry. A single user is served by using singular value decomposition precoding and combining. By approximating the expectations of the channel eigenvalues, we derive upper and lower bounds on the ergodic SE. The obtained upper bound is the best possible system-level performance of any MIMO strategy in non-cooperative cellular networks. We validate our analytical results through simulation. We also conjecture that there exists the optimal number of streams being proportional to the pathloss exponent.