Channel coding may be viewed as the bestinformed and most potent component of cellular communication systems, which is used for correcting the transmission errors inflicted by noise, interference and fading. The powerful turbo code was selected to provide channel coding for Mobile Broad Band (MBB) data in the 3G UMTS and 4G LTE cellular systems. However, the 3GPP standardization group has recently debated whether it should be replaced by Low Density Parity Check (LDPC) or polar codes in 5G New Radio (NR), ultimately reaching the decision to adopt the LDPC code family for enhanced Mobile Broad Band (eMBB) data and polar codes for eMBB control. This paper summarises the factors that influenced this debate, with a particular focus on the Application Specific Integrated Circuit (ASIC) implementation of the decoders of these three codes. We show that the overall implementation complexity of turbo, LDPC and polar decoders depends on numerous other factors beyond their computational complexity. More specifically, we compare the throughput, error correction capability, flexibility, area efficiency and energy efficiency of ASIC implementations drawn from 110 papers and use the results for characterising the advantages and disadvantages of these three codes as well as for avoiding pitfalls and for providing design guidelines.

Abstract-Distributed classification fusion using error-correcting codes (DCFECC) has recently been proposed for wireless sensor networks operating in a harsh environment. It has been shown to have a considerably better capability against unexpected sensor faults than the optimal likelihood fusion. In this paper, we analyze the performance of a DCFECC code with minimum Hamming distance fusion. No assumption on identical distribution for local observations, as well as common marginal distribution for the additive noises of the wireless links, is made. In addition, sensors are allowed to employ their own local classification rules. Upper bounds on the probability of error that are valid for any finite number of sensors are derived based on large deviations technique. A necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity is also established. With the necessary and sufficient condition and the upper error bounds, the relation between the fault-tolerance capability of a DCFECC code and its pair-wise Hamming distances is characterized, and can be used together with any code search criterion in finding the code with the desired fault-tolerance capability. Based on the above results, we further propose a code search criterion of much less complexity than the minimum Hamming distance fusion error criterion adopted earlier by the authors. This makes the code construction with acceptable fault-tolerance capability for a network with over a hundred of sensors practical. Simulation results show that the code determined based on the new criterion of much less complexity performs almost identically to the best code that minimizes the minimum Hamming distance fusion error. Also simulated and discussed are the performance trends of the codes

No abstract

No abstract

This paper proposes a fusion-based cooperative support identification scheme for distributed compressive sparse signal recovery via resource-constrained wireless sensor networks. The proposed support identification protocol involves: (i) local sparse sensing for economizing data gathering and storage, (ii) local binary decision making for partial support knowledge inference, (iii) binary information exchange among active nodes, and (iv) binary data aggregation for support estimation. Then, with the aid of the estimated signal support, a refined local decision is made at each node. Only the measurements of those informative nodes will be sent to the fusion center, which employs a weighted ℓ1-minimization for global signal reconstruction. The design of a Bayesian local decision rule is discussed, and the average communication cost is analyzed. Computer simulations are used to illustrate the effectiveness of the proposed scheme.