Beyond 5G systems are expected to approach 1 Tb/s throughput. This poses a significant challenge to the channel decoder. In this paper, we propose a multi-core architecture based on full row parallel layered LDPC decoder with frame interleaving. Compared with conventional partially parallel layered architectures, the proposed architecture increases the throughput by applying frame interleaving into the pipeline architecture and by using multi-core architectures. Two high rate medium size QC LDPC codes are designed with fast decoding convergence speed for this architecture. Both codes are implemented with single core and multi-core architectures to explore different trade-offs between code design, communication performance and implementation. The four decoders are implemented in 16 nm CMOS FinFET technology with a clock rate of 1 GHz. The placement and routing implementation results show that the single core decoder for the LDPC (1027, 856) code is able to provide 114 Gb/s throughput at maximum 3 iterations with an area of 0.173 mm2and energy efficiency of 1.56 pJ/bit; the multi-core decoder for the (1032, 860) code is able to provide 860 Gb/s throughput at maximum 2 iterations with an area of 1.48 mm2and energy efficiency of 3.24 pJ/bit. The multi-core decoder achieves the highest throughput in the literature for medium size (1-2k) LDPC codes. When compared with other state-of-the-art fully parallel high speed architectures, the proposed architectures bring a significant gain both in area efficiency and energy efficiency while keeping the ability to offer flexibility in code rate, number of iterations and early stop.

A symbol timing synchronization scheme is critical in the design of an OFDM receiver. Large timing errors can result in a loss of orthogonality between subcarriers, ISI and severe bit error degradation. To minimize this degradation, standards incorporate preambles suitable for two kinds of synchronization algorithms: auto-correlation and crosscorrelation. Unfortunately, the performance and complexity tradeoffs between these algorithms have not been well explored. To address this problem, we have built an FPGA implementation of a synchronization system using both autocorrelation and cross-correlation. Based on our results, in this paper we propose a novel cross-correlation synchronizer and hardware architecture. We then compare its performance and complexity to auto-correlation algorithms for HiperLAN/2 and IEEE 802.11a preambles.

The problem of an efficient hardware implementation of multiplications with one or more constants is encountered in many different digital signal-processing areas, such as image processing or digital filter optimization. In a more general form, this is a problem of common subexpression elimination, and as such it also occurs in compiler optimization and many high-level synthesis tasks. An efficient solution of this problem can yield significant improvements in important design parameters like implementation area or power consumption. In this paper, a new solution of the multiple constant multiplication problem based on the common subexpression elimination technique is presented. The performance of our method is demonstrated primarily on a finite-duration impulse response filter design. The idea is to implement a set of constant multiplications as a set of add-shift operations and to optimize these with respect to the common subexpressions afterwards. We show that the number of add/subtract operations can be reduced significantly this way. The applicability of the presented algorithm to the different high-level synthesis tasks is also indicated. Benchmarks demonstrating the algorithm's efficiency are included as well.