Joint application of polar channel coding combined with vector quantization lossy source coding is considered in this paper. The existing index assignment schemes in the literature cannot be used with polar codes due to their unique crossover probabilities. We elaborate on this problem and locally optimize index assignments. In addition, we propose an algorithm that jointly optimizes the number of quantization levels and the rate of the polar code in order to achieve minimum end-to-end distortion. It finds the optimal tradeoff between the distortion caused by channel errors and the quantization distortion. We also derive estimates for the crossover probabilities of the polar code which are required in the analysis. Simulation results confirm the effectiveness of the proposed algorithms and the accuracy of the crossover probabilities.

Random linear coding (RLC) can improve the performance of multicast transmissions in terms of throughput and energy efficiency. However, RLC and linear codes in general cannot necessarily attain the optimal performance in arbitrary networks. In this regard, partial packet recovery can be considered as a nonlinear strategy to complement such approaches for more general networks. In this paper, we propose a partial packet recovery scheme that benefits from the sparsity of bit errors in partially corrupted RLC packets. As opposed to many previous schemes, it performs without introducing preliminary checksums or preambles, demanding physical layer soft information, or requesting post-redundancy from the transmitter. It relies only on algebraic coding and data processing techniques, the existing knowledge at the receiver, and the conventional acknowledgment messages in RLC. By reconstructing and utilizing the partially corrupted packets that are usually discarded, it can reduce the average number of transmitted RLC packets required for successful decoding by typically 50%, which improves throughput and energy efficiency at the transmitter. We formulate our partial packet recovery in the form of a sparse recovery problem, present its different solutions using compressive sensing theory, discuss their complexity, and present and evaluate a Markov chain model for its performance.

To reproduce a band-limited continuous-time signal with optimal fidelity, usually it is sampled at the Nyquist sampling rate and then the sample values are quantized. In nonuniform sampling, the total number of samples are reduced in expense of adding some reconstruction complexity and assuming prior information about the signal. In this paper we propose nonuniform sampling-time with nonuniform bit resolution per sample (bit-depth) to reduce the total required bit budget (i.e. the number of samples timed the bit-depth) even further. This idea is based on the fact that the maximum local variation of band-limited signals is bounded in a given time horizon. Therefore, it is not necessary to allocate a fixed bit-depth proportional to the signal's dynamic range to each sample. Instead, we try to allocate adaptively only the needed number of bits to represent the next sample considering the physical characteristics of the signal. Both sampling and reconstruction entities can generate the next sampling-times and bit-depths locally by observing the current and previous samples only. We propose different techniques in our generalized sampling framework that share a common sampling architecture. Here we only consider ECG signals for evaluation purpose. Based on the simulation results, the total number of required bits to reconstruct the signal with negligible distortion can be reduced up to 88% without imposing any form of transform coding compression.

We propose a novel scheme based on compressive sensing and sparse recovery to boost the performance of cross-packet random linear coding (RLC) by incorporating the partial packets in the decoding algorithm. In conventional RLC schemes, to successfully decode the packets the receiver needs to collect a certain number of correct innovative encoded packets. During this process, there are usually a lot of partially correct packets that are discarded. Our objective is to recover the errors in the partial packets to decrease the total transmitted packets to improve the performance in terms of throughput and energy efficiency. Assuming a systematic RLC, we first formulate this problem in form of a standard sparse recovery problem where the channel errors are sparsely distributed within the packets. Then we show that to correct a certain number of errors at the receiver, the minimum required number of transmitted packets is lower-bounded by the number of partial packets. We show that by correcting and exploiting the partial packets, the required number of RLC transmit packets to successfully deliver a given generation is reduced by typically 57% in comparison with the conventional scheme.

A novel joint source-channel (JSC) coding scheme that combines transform source coding with polar channel codes is proposed with performance gains at low SNRs in terms of bandwidth and energy efficiency. Binary wavelet transform source coding is first applied to decompose the binary source data into a set of components with uneven distributions, particularly, components that are zero with high probability. Normally, a polar code of rate K/N appends N − K redundant bits to the message which can decrease the throughput and introduce an extra energy consumption. By exploiting the binary wavelet transform, the proposed scheme transforms the original signal to the polar code without adding redundant zeros. Hence, the resulting JSC code is of rate one. Since no floating-point arithmetic is required and all operations are performed in GF(2), complexity is vastly reduced which can eventually alleviate the total hardware cost as well as the energy consumption.

In this paper we address the problem of rate scheduling in the Impulse Radio (IR) ultra-wideband (UWB) wireless body area networks (WBANs) and the minimum energy required to stabilize the queuing system. Targeting low complexity WBAN applications, we assume noncoherent receivers based on energy detection and autocorrelation for all nodes. The coordinating node can minimize the average energy consumption of the system and achieve the queue backlog stability of the sensor nodes by controlling the number of pulses per symbol. We first illustrate the necessary and sufficient conditions of network stability for a multi-mode UWB system and then propose a feasible rate scheduling algorithm based on the Lyapunov optimization theory. The scheduling algorithm uses the instantaneous channel state information and the length of the local queue of all sensor nodes and can approach the optimal energy-delay tradeoff of the network. We apply our theoretical framework to the IR-UWB physical layer of the IEEE 802.15.6 standard and extract the optimal physical layer modes that can achieve the desired energy-delay tradeoff.