We investigate information-theoretic limits and design of communication under receiver quantization. Unlike most existing studies that focus on low-resolution quantization, this work is more focused on the impact of weak nonlinear distortion due to resolution reduction from high to low. We consider a standard transceiver architecture, which includes an independent and identically distributed (i.i.d.) complex Gaussian codebook at the transmitter, and a symmetric quantizer cascaded with a nearest neighbor decoder at the receiver. Employing the generalized mutual information (GMI), an achievable rate under general quantization rules is obtained in an analytical form, which shows that the rate loss due to quantization is log (1 + γSNR), where SNR is the signal-to-noise ratio at the receiver front-end, and γ is determined by thresholds and levels of the quantizer. Based on this result, the performance under uniform receiver quantization is analyzed comprehensively. We show that the front-end gain control, which determines the loading factor (normalized one-sided quantization range) of quantization, has an increasing impact on performance as the resolution decreases. In particular, we prove that the unique loading factor that minimizes the mean square error (MSE) of the uniform quantizer also maximizes the GMI, and the corresponding irreducible rate loss is given by log (1 + mmse · SNR), where mmse is the minimum MSE normalized by the variance of quantizer input, and it is equal to the minimum of γ. A geometrical interpretation for the optimal uniform quantization at the receiver is further established. Moreover, by asymptotic analysis, we characterize the impact of biased gain control, showing how small rate losses decay to zero and providing approximations for the achievable rate under large bias. From asymptotic expressions of the optimal loading factor and mmse, approximations and several “per-bit rules” for performance are also provided. Finally we discuss more types of receiver quantization and show that the consistency between achievable rate maximization and MSE minimization does not hold in general.

We investigate performance limits and design of communication in the presence of uniform output quantization with moderate to high resolution. Under independent and identically distributed (i.i.d.) complex Gaussian codebook and nearest neighbor decoding rule, an achievable rate is derived in an analytical form by the generalized mutual information (GMI). The gain control before quantization is shown to be increasingly important as the resolution decreases, due to the fact that the loading factor (normalized one-sided quantization range) has increasing impact on performance. The impact of imperfect gain control in the high-resolution regime is characterized by two asymptotic results: 1) the rate loss due to overload distortion decays exponentially as the loading factor increases, and 2) the rate loss due to granular distortion decays quadratically as the step size vanishes. For a$2 K$-level uniform quantizer, we prove that the optimal loading factor that maximizes the achievable rate scales like$2 \sqrt{\ln (2 K)}$as the resolution increases. An asymptotically tight estimate of the optimal loading factor is further given, which is also highly accurate for finite resolutions.

In this work, generalized nearest neighbor decoding (GNND), a recently proposed receiver architecture, is studied for channels under general input constellations, and multiuser uplink interference suppression is employed as a case study for demonstrating its potential. In essence, GNND generalizes the well-known nearest neighbor decoding, by introducing a symbol-level memoryless processing step, which can be rendered seamlessly compatible with Gaussian channel-based decoders. First, criteria of the optimal GNND are derived for general input constellations, expressed in the form of conditional moments matching, thereby generalizing the prior work which has been confined to Gaussian input. Then, the optimal GNND is applied to the use case of multiuser uplink, for which the optimal GNND is shown to be capable of achieving information rates nearly identical to the channel mutual information. By contrast, the commonly used channel linearization (CL) approach incurs a noticeable rate loss. A coded modulation scheme is subsequently developed, aiming at implementing GNND using off-the-shelf channel codes, without requiring iterative message passing between demodulator and decoder. Through numerical experiments it is validated that the developed scheme significantly outperforms the CL-based scheme.

This paper studies receiver design in single-input multiple-output (SIMO) slow fading channels with imperfect channel state information (CSI) at the receiver only. Using generalized mutual information (GMI) as achievable rate, we study the outage behavior when the receiver employs certain generalized form of the nearest neighbor decoding rule. Our study reveals that linearly shrinking the linear minimum mean-squared error (LMMSE) estimate of the CSI reduces the outage probability when the number of receive antennas is finite. Only in the asymptotic regime where the number of receive antennas grows without bound, the LMMSE estimate of the CSI minimizes the outage probability. Numerical results demonstrate that the proposed linear shrinkage receiver achieves evident outage probability reduction.

We investigate and elaborate upon a folklore in wireless communication systems that, when the nonlinearity at a transmitter is sufficiently weak so that the resulting spectral leakage is at a sufficiently low level, the capacity of the channel (including the transmitter) should be sufficiently close to the ideal channel capacity without transmitter nonlinearity. The context for this study is that effective predistortion techniques have been widely applied to linearize the transmitter nonlinearity in modern wireless communication systems, so as to render the electromagnetic radiation pattern to satisfy stringent spectral regrowth requirements. Based on the quasi-memoryless/memory polynomial model for the transmitter nonlinearity, via an information-theoretic approach, our study affirmatively validates the folklore, and more importantly, characterizes a quantitative relationship between the spectral leakage level and the capacity loss. Specifically, we prove that as the adjacent channel power ratio (ACPR) asymptotically vanishes, the capacity loss is upper bounded by a term that is proportional to the ACPR. We also establish a converse result, and further extend our results to spatial beamforming.

Information transmission over a multiple-input-multiple-output (MIMO) fading channel with imperfect channel state information (CSI) is investigated, under a new receiver architecture which combines the recently proposed generalized nearest neighbor decoding rule (GNNDR) and a successive procedure in the spirit of successive interference cancellation (SIC). Recognizing that the channel input-output relationship is a nonlinear mapping under imperfect CSI, the GNNDR is capable of extracting the information embedded in the joint observation of channel output and imperfect CSI more efficiently than the conventional linear scheme, as revealed by our achievable rate analysis via generalized mutual information (GMI). Numerical results indicate that the proposed scheme achieves performance close to the channel capacity with perfect CSI, and significantly outperforms the conventional pilot-assisted scheme, which first estimates the CSI and then uses the estimated CSI as the true one for coherent decoding.