In this paper, we review some recent advances in the design of ADCs that exploit system-driven metrics, such as the bit-error rate in a communication link, or mutual information in a scheme employing forward error correction. We show, for example, that ADCs can be designed that maximize the information rate between the quantized output of the channel and the input to the channel for communication links with intersymbol-interference and additive noise. These ADCs dramatically outper-form (in terms of achievable information rates) traditional ADC design methods that are based on fixed uniform quantization. Architectures are also developed for ADCs such that system-metrics can be used to dynamically adapt the structure of the ADC to optimize application meaningful criteria, such as bit-error rate for communication over intersymbol interference links.

The multiple-access relay channel with two sources, a single relay, and one destination is considered. Under the assumption of noisy source-relay links causing the relay to be unable to decode without error, we propose a framework for designing one- and two-dimensional quantizers for quantizing the soft information at the relay. These quantizers are mutual-information preserving. Simulation results show a) that mutual-information preserving quantization schemes outperform techniques in which the soft information is forwarded in an analog fashion to the destination, b) that two-dimensional quantization outperforms one-dimensional quantization for source-relay links of different quality, and c) that diversity order of two can be gained in block Rayleigh fading channels by having the relay adaptively select a two-dimensional quantizer from a fixed set of quantizers shared with the destination, depending on the channel state on the source-relay links.

Analog-to-digital converters that maximize the information rate between the quantized channel output sequence and the channel input sequence are designed for discrete-time channels with intersymbol-interference, additive noise, and for independent and identically distributed signaling. Optimized scalar quantizers with Λ regions achieve the full information rate of log2(Λ) bits per channel use with a transmit alphabet of size Λ at infinite signal-to-noise ratio; these quantizers, however, are not necessarily uniform quantizers. Low-precision scalar and two-dimensional analog-to-digital converters are designed at finite signal-to-noise ratio, and an upper bound on the information rate is derived. Simulation results demonstrate the effectiveness of the designed quantizers over conventional quantizers. The advantage of the new quantizers is further emphasized by an example of a channel for which a slicer (with a single threshold at zero) and a carefully optimized channel input with memory fail to achieve a rate of one bit per channel use at high signal-to-noise ratio, in contrast to memoryless binary signaling and an optimized quantizer.

We consider the discrete-time channel with intersymbol-interference and additive noise under output analog-to-digital conversion (quantization) at the receiver. The analog-to-digital converter is optimized so as to maximize the information rate between the quantized channel output sequence and the channel input sequence, where the input sequence has independent and identically distributed symbols. An upper bound on the information rate is derived. Simulation results demonstrate the effectiveness of the designed quantizers over conventional quantizers at 1-bit/sample precision. The advantage of those quantizers is further emphasized by an example of a channel for which a simple slicer and a carefully optimized channel input with memory fail to achieve a rate of one bit per channel use at high signal-to-noise ratio, in contrast to memoryless binary signaling and an optimized quantizer.

The orthogonal multiple-access relay channel with two sources is considered. The goal of this paper is to show the applicability and effectiveness of a previously introduced quantize-and-forward scheme to a more realistic channel and system model, including orthogonal frequency division multiple access and multipath fading channels. Simulation results are provided to demonstrate the gain of quantize-and-forward relayed communication as opposed to the point-to-point links without the relay.

The source coding rate allocation problem for the orthogonal multiple-access relay channel with M users and compress-and-forward at the relay is addressed. In case of Gaussian codebooks at the sources and Gaussian channels, we show that the sum-rate-optimal assignment of source coding rate at the relay is given by waterfilling. For general modulation alphabets at the sources and finite-alphabet discrete memoryless channels, the source coding rate allocation problem is formulated using the information bottleneck method, based on which we appropriately modify a standard cutting-plane algorithm to numerically compute an optimal source coding rate vector at the relay.

We consider the uplink of two mobile stations with the help of one common relay. Recently, joint network-channel coding based on LDPC codes was proposed for this setup where the channel codes at the mobile stations and the network code at the relay form one distributed LDPC code. This approach allows to gain diversity with a higher rate compared to a relay system without network coding. We propose a practical joint network-channel code that follows the structure of the WiMax IEEE 802.16e LDPC code, and that benefits from efficient linear time encoding and reduced storage requirements. We show how to use a modified high girth code at the relay that allows to gain diversity. Further, we compare two decoding methods of the distributed code at the destination. Simulation results for an example with noisy block fading channels against reference systems demonstrate the advantage of the proposed design.

A network with two sources, one relay, and one destination is considered. Under the assumption of noisy source- relay links causing the relay to be unable to decode without error, we propose a quantizer design framework where the quantizer jointly compresses the soft information available for both sources at the relay. The quantizer design is based on the information bottleneck method using the notion of relevant information as an optimization criterion.

We consider a wireless relay network where two sources transmit independent information on mutually orthogonal channels to a common destination with the help of one relay. Based on the expression for the achievable rate in such a network for compress-and-forward relaying without Wyner-Ziv coding, we design mutual-information preserving quantizers for compression at the relay. In the proposed relaying scheme, both the relay and the destination share a fixed set of quantizers, among which the relay selects a suitable one depending on the channel quality on the source-relay links for compression of its received values. Simulations performed in Rayleigh block fading channels reveal that full diversity order of two can be achieved using that scheme. We also comment on the size of the quantizer set and the associated signaling overhead.

We consider universal piecewise linear regression of real valued bounded sequences under the squared loss function. In this setting, we present a lower bound on the regret of a universal sequential piecewise linear regressor compared to the best piecewise linear regressor that has access to the entire sequence in advance. This lower bound is tight in that it achieves the corresponding upper bound, suggesting a minmax optimality of the sequential regressor, for every individual bounded sequence.