A physical-layer network coding (PNC) scheme is developed using serially concatenated continuous phase modulation (SCCPM) with symbol interleavers in a two-way relay channel (TWRC), i.e., SCCPM-PNC. The decoding structure of the relay is designed and the corresponding soft input soft output (SISO) iterative decoding algorithm is discussed. Simulation results show that the proposed SCCPM-PNC scheme performs good performance in bit error rate (BER) and considerable improvements can be achieved by increasing the interleaver size and number of iterations.

This letter presents a technique to reduce the complexity of the soft-output multiple-input multiple-output symbol detection based on Dijkstra's algorithm. By observing that the greedy behavior of Dijkstra's algorithm can entail unnecessary tree-visits for the symbol detection, this letter proposes a technique to evict non-promising candidates early from the search space. The early eviction technique utilizes layer information to determine if a candidate is promising, which is simple but effective. When the SNR is 30dB for 6×6 64-QAM systems, the average number of tree-visits in the proposed method is reduced by 72.1% in comparison to that in the conventional Dijkstra's algorithm-based symbol detection without the early eviction.

An adaptive zero forcing maximum likelihood soft input soft output (AZFML-SISO) detector for multiple input multiple output (MIMO) wireless systems is presented. Its performance in an iterative MIMO receiver is analyzed. The AZFML-SISO detector calculates the soft outputs, applying the ML approach to the list that contains only those signal vectors limited by a hypersphere around the zero forcing (ZF) solution. The performance of the algorithm is evaluated on a communication system based on the standard for single carrier broadband wireless communication IEEE 802.16, with three transmit and three receive antennas. It is shown by computer simulation that the computational complexity in an average sense of the receiver running the AZFML-SISO algorithm is reduced by 90% at the SNR values of 30 dB and by 50% for SNR values of 15 dB in comparison to the receiver with an ML detector, while the system performance degrades by less than 1 dB.

It has been shown that the output information produced by the soft output Viterbi algorithm (SOVA) is too optimistic. To compensate for this, the output information should be normalized. This letter proposes a simple normalization technique that extends the existing sign difference ratio (SDR) criterion. The new normalization technique counts the sign differences between the a-priori information and the extrinsic information, and then adaptively determines the corresponding normalization factor for each data block. Simulations comparing the new technique with other well-known normalization techniques show that the proposed normalization technique can achieve about 0.2 dB coding gain improvement on average while reducing up to about 1/2 iteration for decoding.

Two implementation schemes for a two-step SOVA (Soft Output Viterbi Algorithm) decoder are proposed and verified in a chip. One uses the combination of trace back (TB) logic to find the survivor state and double trace back logic to find the weighting factor of a two-step SOVA. The other is that the reliability values are divided by a scaling factor in order to compensate for the distortion brought by overestimating those values in SOVA. We introduced a fixed scaling factor of 0.25 or 0.33 for a rate 1/3 and designed an 8-state Turbo decoder with a 256-bit frame size to lower the reliability values. The implemented architecture of the two-step SOVA decoder allows important savings in area and high-speed processing compared with the one-step SOVA decoder using register exchange (RE) or trace-back (TB) method. The chip is fabricated using 0.65 µm gate array at Samsung Electronics and it shows higher SNR performance by 2 dB at the BER 10-4 than that of a two-step SOVA decoder without a scaling factor.