In this paper, we develop a new capacity-approaching code, namely, parallel-concatenated (PC)-Low Density Parity Check (LDPC) convolutional code that is based on the parallel concatenation of trellis-based quasi-cyclic LDPC (TQC-LDPC) convolutional codes. The proposed PC-LDPC convolutional code can be derived from any QC-LDPC block code by introducing the trellis-based convolutional dependency to the code. The capacity-approaching PC-LDPC convolutional codes are encoded through parallel concatenated trellis-based QC recursive systematic convolutional (RSC) encoder (namely, QC-RSC encoder) that is also proposed in this paper. The proposed PC-LDPC convolutional code and the associated encoder retain a fine input granularity on the order of the lifting factor of the underlying block code. We also describe the corresponding trellis-based QC maximum a posteriori probability (namely, QC-MAP) decoder that efficiently decodes the PC-LDPC convolutional code. Performance and hardware implementation results show that the PC-LDPC convolutional codes with the QC-MAP decoder have two times lower complexity for a given bit-error-rate (BER), signal-to-noise ratio, and data rate, than conventional QC-LDPC block codes and LDPC convolutional codes. Moreover, the PC-LDPC convolutional code with the QC-MAP decoder outperforms the conventional QC-LDPC block codes by more than 0.5 dB for a given BER, complexity, and data rate and approaches Shannon capacity limit with a gap smaller than 1.25 dB. This low decoding complexity and the fine granularity make it feasible to efficiently implement the proposed capacity-approaching PC-LDPC convolutional code and the associated trellis-based QC-MAP decoder in next generation ultra-high data rate mobile systems.

In this paper, we propose a new type of code called Trellis-based Quasi-Cyclic (TQC)-LDPC convolutional code, which is a special case of protograph-based LDPC convolutional codes. The proposed TQC-LDPC convolutional code can be derived from any QC-LDPC block code by introducing trellis-based convolutional dependency to the code. The main advantage of the proposed TQC-LDPC convolutional code is that it allows reduced decoder complexity and input granularity (which is defined as the minimum number of input information bits the code requires to generate a codeword) while maintaining the same bit error-rate as the underlying QC-LDPC block code ensemble. We also propose two related power-efficient encoding methods to increase the code rate of the derived TQC-LDPC convolutional code. The newly derived short constraint length TQC-LDPC convolutional codes enable low complexity trellis-based decoders and one such decoder is proposed and described in this paper (namely, QC-Viterbi). The TQC-LDPC convolutional codes and the QC-Viterbi decoder are compared to conventional LDPC codes and Belief Propagation (BP) iterative decoders with respect to bit-error-rate (BER), signal-to-noise ratio (SNR), and decoder complexity. We show both numerically and through hardware implementation results that the proposed QC-Viterbi decoder outperforms the BP iterative decoders by at least 1 dB for same complexity and BER. Alternatively, the proposed QC-Viterbi decoder has 3 times lower complexity than the BP iterative decoder for the same SNR and BER. This low decoding complexity, low BER, and fine granularity makes it feasible for the proposed TQC-LDPC convolutional codes and associated trellis-based decoders to be efficiently implemented in high data rate, next generation mobile systems.

LDPC codes are becoming popular in next generation high throughput wireless standards since they can provide a level of parallelism with sufficient performance to support the high gigabit rate. In this paper, we propose a new method for LDPC decoding called Parallel Processing Layered (PPL). The new method aims to optimize the latency and power efficiency of LDPC decoding to enable significant increase of the processing rate, thereby saving battery power for mobile devices. We provide performance results in different channel models using the newly defined WiGig standards, and compare them to the conventional decoding methods. We show that the new proposed LDPC decoding architecture converges 2x faster than conventional (i.e. Flooding) methods.

This paper examines pilot-aided channel estimation algorithm for multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in rapid time-variant environments. It includes Doppler frequency estimation, delay path profile estimation and interpolation algorithms in time and frequency domains. The algorithm performs channel statistics estimation for each frame and uses the pilot symbols of this frame where all signals pass through the same propagation channel. The proposed algorithm uses separable, interpolation order changing to reduce channel estimation complexity. This algorithm first applies interpolation in the time domain and then in the frequency domain. Hence, for the pilot pattern of IEEE 802.16 e we get twice as many reference symbols for frequency interpolation, which improves the interpolation quality, especially for channels with long delay profiles. Computer simulation of the V-BLAST structure demonstrates high effectiveness of this algorithm and its robustness to increasing subscriber speed. Packet error rate (PER) degradation, in comparison with the ideal channel, is l-2.5 dB for 4 x 4 mode and 0.5-1.5 dB for 2 x 2 mode. Our results show that the performance degradation is acceptable not only up to 250 km/h, but also up to 500 km/h.

This paper introduces a simple but efficient reception algorithm for the MIMO systems with spatial multiplexing or Bell Laboratories Layered Space-Time (BLAST) architecture and soft decoding. We also analyze the optimal maximum likelihood algorithm as well as the minimum mean squared error (MMSE) and MMSE with ordered successive interference cancellation (MMSE-OSIC) algorithms. The performance of these algorithms at the decoder input and output when soft and hard decoding are used is studied. A novel simple MMSE with soft decisions enhancement (MMSE-SDE) algorithm is proposed with improved characteristics for soft decoding. This algorithm provides up to 3 dB gain over MMSE and MMSE-OSIC in the ill-conditioned MIMO channel.

Orthogonal variable spreading factor (OVSF) code sare widely used to provide variable data rates for supporting different band width requirements in wideband code division multiple access (WCDMA) systems. Many works in the literature have intensively investigated code placement and replacement schemes in OVSF code trees to reduce the code blocking probability and increase the code spectral efficiency. This paper describes a method for dynamic allocation of OVSF codes when a base station transceiver (BTS) is equipped with an adaptive antenna array (AAA) utilizing spatial diversity of mobile users to support more users in WCDMA cellular network. In our method, the new user is assigned an OVSF code of an active user whose downlink beam has the smallest spatial correlation with the channel vector of the new user. The performance of the dynamic OVSF code assignment with the AAA (SA-DCA) is justified through computer simulations under varying traffic load conditions. The results show that the SADCA method significantly reduces call blocking probability and improves spectral efficiency in the system when compared to the conventional (CCA) and dynamic (DCA) OVSF code assignment schemes without AAA.

This paper presents the results from analysis of the forward link capacity for the third generation CDMA wireless system employing adaptive antenna arrays (AAA) at the base station. The capacity performance for mixed traffic scenarios of the forward link is studied in a multi-cell CDMA network including the array topology, multipath angle spread, and mean DOA beamforming technique. The effect of fast fading, log-normal shadowing, multiple access interference, and thermal noise is considered. The simulation results show that with the AAA, system capacity can be increased several fold compared to non-adaptive antenna base stations.