Scaling Factor Optimization of Turbo-Polar Iterative Decoding

In this paper, we investigate the weighted iterative decoding to improve the performance of turbo-polar code. First of all, a minimum weighted mean square error criterion is proposed to optimize the scaling factors(SFs). Secondly, for two typical iterative algorithms,such as soft cancellation(SCAN) and belief propagation(BP) decoding, genie-aided decoders are proposed as the ideal reference of the practical decoding. Guided by this optimization framework, the optimal SFs of SCAN or BP decoders are obtained. The bit error rate performance of turbo-polar code with the optimal SFs can achieve 0.3 dB or 0.7 dB performance gains over the standard SCAN or BP decoding respectively.

Unsourced Multiple Access for 6G Massive Machine Type Communications

Unsourced multiple access(UMA)is a multi-access technology for massive,low-power,uncoordinated,and unsourced Machine Type Communication(MTC)networks.It ensures transmission reliability under the premise of high energy efficiency.Based on the analysis of the 6G MTC key performance indicators(KPIs)and scenario characteristics,this paper summarizes its requirements for radio access networks.Following this,the existing multiple access models are analyzed under these standards to determine UMA's advantages for 6G MTC according to its design characteristics.The critical technology of UMA is the design of its multiple-access coding scheme.Therefore,the existing UMA coding schemes from different coding paradigms are further summarized and compared.In particular,this paper comprehensively considers the energy efficiency and computational complexity of these schemes,studies the changes of the above two indexes with the increase of access scale,and considers the trade-off between the two.It is revealed by the above analysis that some guiding rules of UMA coding design.Finally,the open problems and potentials in this field are given for future research.

A survey on one-bit compressed sensing： theory and applications

In the past few decades, with the growing popularity of compressed sensing （CS） in the signal processing field, the quantization step in CS has received significant attention. Current research generally considers multi-bit quantization. For systems employing quantization with a sufficient number of bits, a sparse signal can be reliably recovered using various CS reconstruction algorithms. Recently, many researchers have begun studying the one- bit quantization case for CS. As an extreme case of CS, one- bit CS preserves only the sign information of measurements, which reduces storage costs and hardware complexity. By treating one-bit measurements as sign constraints, it has been shown that sparse signals can be recovered using certain reconstruction algorithms with a high probability. Based on the merits of one-bit CS, it has been widely applied to many fields, such as radar, source location, spectrum sensing, and wireless sensing network. In this paper, the characteristics of one-bit CS and related works are reviewed. First, the framework of one-bit CS is introduced. Next, we summarize existing reconstruction algorithms. Additionally, some extensions and practical applications of one-bit CS are categorized and discussed. Finally, our conclusions and the further research topics are summarized.

Tighter bounds of multihop relayed communications in Nakagami-m fading

Closed-form bounds for the end-to-end performance of multihop communications with non-regenerative relays over Nakagami-m fading channels are investigated.Upper and lower bounds of the end-to-end signal-to-noise ratio(SNR)are first developed by using the monotonicity.Then,the probability density functions(PDFs),the cumulative distribution functions,and the momentgenerating functions(MGFs)of the bounds are derived.Using these results,the bounds for the outage and average bit error probability(ABEP)are obtained.Numerical and simulation results are executed to validate the tightness of the proposed bounds.