In this work, a high performance LDPC decoder architecture is presented. It is a partially-parallel architecture for low-complexity consideration. In order to eliminate the idling time and hardware complexity in conventional partially-parallel decoders, the decoding process, decoder architecture and memory structure are optimized. Particularly, the parity-check matrix is optimally partitioned into four unequal sub-matrices that lead to high efficiency in hardware sharing. As a result, it can handle two different codewords simultaneously with 100% hardware utilization. Furthermore, for minimizing the performance loss due to round-off errors in fixed-point implementations, the well-known modified min-sum decoding algorithm is enhanced by our recently proposed high-performance CMVP decoding algorithm. Overall, the proposed decoder has high throughput, low complexity, and good BER performances. In the circuit implementation example of the (576,288) parity check matrix for IEEE 802.16e standard, the decoder achieves a data rate of 5.5 Gbps assuming 10 decoding iterations and 7 quantization bits, with a small area of 653 K gates, based on UMC 90 nm process technology.

Conventional symbol time (ST) synchronization algorithms for orthogonal frequency-division multiplexing (OFDM) systems are mostly based on the maximum correlation result of the cyclic prefix. Due to the channel effect, the estimated ST is not accurate enough. Hence, one needs to further identify the channel impulse response (CIR) so as to obtain a better ST estimation. Overall, the required computational complexity is high because it involves time-domain (TD) correlation operations, as well as the fast Fourier transform (FFT) and inverse FFT (IFFT) operations. In this work, without the FFT/IFFT operations and the knowledge of CIR, a low-complexity TD ST estimation is proposed. We first characterize the frequency-domain (FD) interference effect. Based on the derivation, the new method locates the symbol boundary at the sampling point with the minimum interference in the FD (instead of the conventional maximum TD correlation result). Moreover, to reduce the computational complexity, the proposed FD minimum-interference (MI) metric is converted to a low-complexity TD metric by utilizing Parseval's theorem and the sampling theory. Simulation results exhibit good performance for the proposed algorithm in multipath fading channels.

This work first investigates two existing check node unit (CNU) architectures for LDPC decoding: self-message-excluded CNU (SME-CNU) and two-minimum CNU (TM-CNU) architectures, and analyzes their area and timing complexities based on various realization approaches. Compared to TM-CNU architecture, SME-CNU architecture is faster in speed but with much higher complexity for comparison operations. To overcome this problem, this work proposes a novel systematic optimization algorithm for comparison operations required by SME-CNU architectures. The algorithm can automatically synthesize an optimized fast comparison operation that guarantees a shortest comparison delay time and a minimized total number of 2-input comparators. High speed is achieved by adopting parallel divide-and-conquer comparison operations, while the required comparators are minimized by developing a novel set construction algorithm that maximizes shareable comparison operations. As a result, the proposed design significantly reduces the required number of comparison operations, compared to conventional SME-CNU architectures, under the condition that both designs have the same speed performance. Besides, our preliminary hardware simulations show that the proposed design has comparable hardware complexity to low-complexity TM-CNU architectures.

In OFDM receivers, an accurate signal-to-noise ratio (SNR) estimation is desirable for all sorts of operations involved in high-performance baseband demodulation. In this work, conventional SNR estimation techniques are investigated. Next, a blind SNR estimation scheme for the phase-shift keying (PSK) signals based on the coherence function is proposed. The proposed method is non-data-aided (NDA) and hence bandwidth-efficient. Simulations confirm that the proposed method has the best performance from moderate to high SNRs both in AWGN and dispersive channels; however, the proposed method has worse performance when SNRs are low.