This paper proposes performance improvement schemes for non-coherent multiple-input multiple-output (MIMO) communication systems employing per transmit antenna differential mapping (PADM). PADM is one form of differential space-time coding (DSTC), which generates an independent differentially encoded sequence for each of the multiple transmit antennas by means of space-time coding and mapping. The features of the proposed schemes are as follows: 1) it employs an asymmetric space-time mapping instead of the conventional symmetric space-time mapping in order to lower the required signal to noise power ratio (SNR) for maintaining the bit error rate (BER) performance; 2) it employs an analytically derived branch metric criterion based on channel prediction for per-survivor processing (PSP) in order to track fast time-varying channels. Finally, computer simulation results confirm that the proposed schemes improve the required SNR by around 1dB and can track at the maximum Doppler frequency normalized by symbol rate of 5%.

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.

This letter proposes a low-complexity soft-detection algorithm for modified dual-carrier modulation (MDCM) in WiMedia ultra-wideband (UWB) systems. In order to reduce the complexity of soft-output maximum-likelihood detection (soft-MLD), which gives the optimal performance for MDCM symbols, the proposed algorithm utilizes the following three methods: real/imaginary separation, multiplierless distance calculation, and candidate set reduction. Through these methods, the proposed algorithm reduces the complexity of soft-MLD by 97%, while preventing the deterioration of its optimality. The performance of the proposed algorithm is demonstrated by simulations of 640–1024 Mbps transmission modes of the latest Release 1.5 standard of the WiMedia UWB.

This paper proposes non-coherent multiple-input multi-ple-output (MIMO) communication systems employing per transmit antenna differential mapping (PADM), which generates an independent differentially encoded sequence for each of the multiple transmit antennas by means of space-time coding and mapping. At a receiver, the proposed PADM employs adaptive maximum-likelihood detection (MLD). The features of PADM are as follows: 1) it has excellent tracking performance for fast time-varying fading channels, because it can detect transmitted data without needing channel state information (CSI); 2) it can be applied not only to transmit diversity (TD) but also to spatial multiplexing (SM). In this paper, we analyze the adaptive MLD based on pseudo matrix inversion and derive its metric for data detection. In order to satisfy requirements on multiple transmitted sequences for the adaptive MLD, this paper proposes a mapping rule for PADM. Next, this paper describes a receiver structure based on per-survivor processing (PSP), which can drastically reduce the complexity of adaptive MLD. Finally, computer simulations confirm that the proposed non-coherent MIMO communication systems employing PADM have excellent tracking capability for TD and SM on fast time-varying fading channels.

This paper proposes an efficient list extension algorithm for soft-output multiple-input-multiple-output (soft-MIMO) detection. This algorithm extends the list of candidate vectors based on the vector selected by initial detection, in order to solve the empty-set problem, while reducing the number of additional vectors. The additional vectors are obtained from multiple detection orders, from which high-quality soft-output can be generated. Furthermore, a method to reduce the complexity of the determination of the multiple detection orders is described. From simulation results for a 44 system with 16- and 64-quadrature amplitude modulations (QAM) and rate 1/2 and 5/6 duo-binary convolutional turbo code (CTC), the soft-MIMO detection to which the proposed list extension was applied showed a performance degradation of less than 0.5 dB at bit error rate (BER) of 10-5, compared to that of the soft-output maximum-likelihood detection (soft-MLD) for all code rate and modulation pairs, while the complexity of the proposed list extension was approximately 38% and 17% of that of an existing algorithm with similar performance in a 44 system using 16- and 64-QAM, respectively.

An efficient pruning method is proposed for the infinity-norm sphere decoding based on Schnorr-Euchner enumeration in multiple-input multiple-output spatial multiplexing systems. The proposed method is based on the characteristics of the infinity norm, and utilizes the information of the layer at which the infinity-norm value is selected in order to decide unnecessary sub-trees that can be pruned without affecting error-rate performance. Compared to conventional pruning, the proposed pruning decreases the average number of tree-visits by up to 37.16% in 44 16-QAM systems and 33.75% in 66 64-QAM systems.

This letter deals with computationally efficient maximum-likelihood (ML) detection for the quasi-orthogonal space-time block code (QOSTBC) with four transmit antennas. The proposed ML detector uses a permutation based real-valued equivalent channel matrix representation. As a result, the complexity of ML detection problem is moderated from O(2|A|2) to O(4|A|), where |A| is modulation order. Numerical results show that the proposed ML detector provides ML performance and achieves greatly high computational savings.

In this paper, a computationally efficient stack-based iterative detection algorithm is proposed for V-BLAST systems. To minimize the receiver's efforts as much as possible, the proposed scheme employs iterative tree search for complexity reduction and storage saving. After an M-ary tree structure by QR decomposition of channel matrix is constructed, the full tree depth is divided into the first depth and the remaining ones. At tree depth of one, the proposed algorithm finds M candidate symbols. Based on these symbols, it iteratively searches the remaining symbols at second-to-last depth, until finding an optimal symbol sequence. Simulation results demonstrate that the proposed algorithm yields the performance close to that of sphere detection (SD) with significant saving in complexity and storage.

We developed two types of practical maximum-likelihood detectors (MLD) for multiple-input multiple-output (MIMO) systems, using a field programmable gate array (FPGA) device. For implementations, we introduced two simplified metrics called a Manhattan metric and a correlation metric. Using the Manhattan metric, the detector needs no multiplication operations, at the cost of a slight performance degradation within 1 dB. Using the correlation metric, the MIMO-MLD can significantly reduce the complexity in both multiplications and additions without any performance degradation. This paper demonstrates the bit-error-rate performance of these MLD prototypes at a 1 Gbps-order real-time processing speed, through the use of an all-digital baseband 44 MIMO testbed integrated on the same FPGA chip.

This paper discusses a generalized concept of multiple-symbol differential detection (MDD) and analytically derives weight parameters based on channel prediction for MDD on fast time-varying channels. At first, this paper shows that adaptive maximum-likelihood sequence estimation employing per-survivor processing (PSP-MLSE) with a single channel tap is similar concept to MDD. Next, the weight parameters for MDD are derived according to the channel estimation of PSP-MLSE based on a high order channel prediction. Finally, computer simulation confirms that MDD with the analytically derived parameters mitigates floor of bit error rate (BER) on fast time-varying fading channels without channel state information.

This paper proposes a survivor-correction Viterbi algorithm (SCVA) and presents its application to an iterative sequence estimation in order to improve bit error rate performance of decision-feedback sequence estimation (DFSE) in the presence of intersymbol interference. The SCVA can mitigate erroneous survivor selections due to DFSE, because it modifies the add-compare-select operation to an add-correct-compare-select operation. Finally, it is confirmed by computer simulation that complexity of the proposed scheme is independent of delay of the main delayed ray and its performance is superior to that of DFSE at the same number of states.