The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multiuser underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.

Turbo codes have outstanding error correction performance. However, the implementations of the iterative turbo decoder require substantial memory and incur a long latency which cannot be tolerated in some applications. In this paper, various kinds of high throughput turbo decoding schemes are introduced, and we propose a new turbo decoding scheme that using the advantages of each scheme. The proposed scheme is basically based on the sliding window, double flow and shuffled turbo decoding scheme. Simulation results show that the proposed scheme offers good BER performance with less clock cycles per iteration than existing turbo decoding schemes. We also show that the required memories can be reduced by adjusting the size of window properly.

In this correspondence, the stopping set of turbo codes with iterative decoding in the binary erasure channel is defined. Block and bit erasure probabilities of turbo codes are studied by using the stopping set analysis. It is found that block and bit erasure probabilities of turbo codes with iterative decoding are higher than those with maximum-likelihood decoding, where the differences are negligible in the error floor region. It is shown that in the error floor region, block and bit erasure probabilities of turbo codes with iterative decoding are dominated by small stopping sets and are asymptotically dominated by low weight codewords.

We propose the signal-to-noise ratio (SNR) transfer characteristic band (TCB) chart of extrinsic information (EI) as a basic tool for the probabilistic convergence analysis of iterative decoding. The proposed SNR TCB chart is a general version of the widely used SNR transfer chart, and it can model the behavior of EI in iterative decoding with any blocklength. We propose an algorithm using the SNR TCB chart to obtain the information, which is both quantitative and qualitative, on the probabilistic convergence behavior of iterative decoding. We also propose the bit-error rate (BER) estimate, in the form of a lower bound, of turbo codes over an additive white Gaussian noise channel. The proposed estimate is especially useful in estimating the BER of finite-length turbo codes in a waterfall region, while it is meaningful for all blocklengths and all Eb/N0

We propose a tool for the analysis of the finite-length iterative turbo decoding algorithm. The proposed tool is a generalized EXIT (extrinsic information transfer) chart based on the mutual information transfer characteristics of the extrinsic information in the iterative turbo decoding algorithm. The proposed tool can describe the probabilistic convergence behavior of the iterative decoding algorithm. By using this tool, we obtain the BER estimates of the finite-length turbo codes in the form of a lower bound, which shows a gentle waterfall over a wide waterfall region. The obtained lower bound is in reasonable agreement with the BER obtained by simulations of the iterative decoding algorithm.

A novel method of estimating the BER performance of finite length turbo codes in a binary AWGN channel is proposed. We consider the asymptotes of BER performance at low and high E/sub b//N/sub 0/ resulted from the small and large extrinsic values, respectively. The BER performance curve transitions between two asymptotes in the waterfall region, where the transition probability is approximately obtained by using the SNR transfer characteristic band of extrinsic values in the turbo decoding algorithm. Then, the BER estimate of finite length turbo codes is obtained by using two asymptotes at low and high E/sub b//N/sub 0/ and the transition probability between them.