In this paper, the segmentation-based order statistics decoding and the partial-order statistics decoding are proposed as low-complexity soft-decision decoding schemes for linear binary block codes of small to medium block length. The bit error rate performance and complexity of these decoding schemes are shown to outperform, in some cases, the original order statistics decoding. Furthermore, it is conjectured that the bit error rate performance can be improved with a small implementation cost by enhancing the receiver front-end, for example, using multiple receiving antennas, rather than improving the performance by employing more complex forward error correction codes.978-1-4244-5668-0/09/$25.00

With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication capability are required for future-generation systems such as millimeter-wave LANs, broadband wireless access schemes, and 5G/6G networks, among other things. Effectual congestion control is regarded as of the essential aspects of 5G/6G technology. It permits operators to run many network illustrations on a single organization while maintaining higher service quality. A sophisticated decision-making system for arriving network traffic is necessary to confirm load balancing, limit network slice letdown, and supply alternative slices in slice letdown or congestion. Because of the massive amount of data being generated, artificial intelligence (AI) and machine learning (ML) play a vital role in reconfiguring and improving a 5G/6G wireless network. In this research work, a hybrid deep learning method is being applied to forecast optimal congestion improvement in the wireless sensors of 5G/6G IoT networks. This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. The proposed approach provided promising results, with 0.933 accuracy, and 0.067 miss rate.

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The ordered statistics-based list decoding techniques for linear binary block codes of small to medium block length are investigated. The construction of a list of the test error patterns is considered. The original ordered-statistics decoding (OSD) is generalized by assuming segmentation of the most reliable independent positions (MRIPs) of the received bits. The segmentation is shown to overcome several drawbacks of the original OSD. The complexity of the ordered statistics-based decoding is further reduced by assuming a partial ordering of the received bits in order to avoid the complex Gauss elimination. The probability of the test error patterns in the decoding list is derived. The bit error rate performance and the decoding complexity trade-off of the proposed decoding algorithms is studied by computer simulations. Numerical examples show that, in some cases, the proposed decoding schemes are superior to the original OSD in terms of both the bit error rate performance as well as the decoding complexity.

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