Low complexity iterative receiver for linear precoded OFDM

For cellular networks, the increased cell density and decreased cell size will give rise to significant inter-cell interference. In this work, an adaptive antenna array and an iterative receiver are combined to facilitate inter-cell interference suppression and cancellation at the receiver end. Iterative receivers exchange soft information between different receiver modules, e.g., symbol detector and channel decoder. An adaptive antenna array is integrated within the iterative receiver, which performs interference suppression and successive interference cancellation. The beamforming weight vectors are updated in each iteration using interference rejection combing, which takes into account the soft information from the channel decoder and previously estimated interfering signals. The proposed algorithm can effectively suppress and cancel the interference signal with various signal-to-interference ratios under different channel models.

We develop iterative turbo receivers for time-variant fading channels which jointly perform channel and frequency offset estimation together with data detection and decoding. Three versions of the joint estimator, the Bayesian, the maximum likelihood and the regularised-maximum likelihood are presented depending on how much knowledge of channel statistics is available. The estimation and detection are based on the basis expansion model of the fading time variations and use the di-chotomous search frequency estimation technique. Soft information generated in the turbo decoder is used to improve the quality of detection in the subsequent iterations. Simulation results show that the proposed receivers provide as good performance as the corresponding ones operating with perfect knowledge of the frequency offset.

This paper presents some results on MMSE turbo equalization applied to real sea water trials in the Atlantic ocean. High order modulations (8-PSK, 16-QAM and 32-QAM) have been tested. The channel data rate on the underwater acoustic channel is up to 20 kbps. The receiver includes timing recovery, equalization, interleaving and channel coding. The results which are evaluated in term of bit error rate (BER) and mean square error (MSE) show that turbo equalization improves the performance of the receiver and allows reliable high order transmissions when the signal to noise ratio is sufficient.

In this paper, we propose a joint design of MIMO-PNC (multiple-input multiple-output physical layer network coding) and channel decoding in two way relay channels. The paper shows that if we adopt the same Repeat Accumulate (RA) channel code at the two end nodes, there would be a compatible decoder at the relay which can transform the received superimposed packet to the network coding form of the two packets efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the MIMO PNC channel. The simulation results show that our new scheme outperforms the previously proposed schemes which combined MIMO NC with channel coding/decoding significantly in terms of BER with little added complexity.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, time-varying multipath fading of channel destroys the orthogonality among subcarriers and leads to serious intercarrier interference (ICI). The system performance degrades more severely as normalized Doppler frequency increases. In order to mitigate the effect of time-varying fading, a low-complexity iterative receiver with joint ICI cancellation and pilot-assisted channel estimation is proposed. The initial channel state information (CSI) is estimated by performing time-domain interpolation and least-square (LS) method on the received pilot symbols. The soft outputs are obtained from the decoders after low-complexity linear minimum mean-square error (LC-LMMSE) detection. In the following stages, the soft outputs are feedback to update the CSI estimation. Furthermore, a ¿linear statistics combining¿ (LSC) technique is used to improve the performance of the proposed equalizer by combining the outputs of LC-LMMSE and parallel interference canceler (PIC) with weighting coefficients estimated by maximizing the signal to interference-plus-noise ratio (SINR) at the output of LSC. The complexity of system is significantly reduced by restricting the interference to neighboring subcarriers and employing the LC-LMMSE by limiting the frequency-domain CSI into diagonal region. The simulation results show that the proposed iterative receiver with estimated CSI approaches the ICI-free bound even at very high mobility scenarios.

In this paper, we consider a generic model of space-time bit-interleaved coded modulation (ST-BICM) on a multiple-input multiple-output (MIMO) Rayleigh fading multipath channel. A practical low-complexity receiver structure performing iteratively MIMO data detection, channel decoding and channel estimation, is presented. The MIMO data detection, employing a reduced-state list-type soft output Viterbi algorithm enables to cope with severe channel intersymbol interference (ISI) without MIMO prefiltering. Among other results, simulations show that our approach can dramatically improve the downlink performance of time-division multiple access (TDMA) systems with high order modulation, keeping a reasonable complexity at the receiver side.

3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) Advanced standards are currently under way, where 4×4 MIMO (Multiple Input Multiple Output) scheme is the minimum structure. One of the best solutions to support the maximum data rate transmission is to adopt a MIMO scheme, especially spatial multiplexing mode, which means that each antenna port can transmit separate data. To realize this algorithm, the main issue is to cancel interferences added to each antenna port, and therefore, the algorithm at the receiver should have good performances. In the aspect of implementation, MMSE (Minimum Mean Square Error) detection algorithm is a reasonable method considering both complexity and performance. Furthermore, an iterative receiver (IR) algorithm can also be applied to this system. An IR algorithm has a tradeoff between complexity and performance. In this paper, we designed a reasonable and implementable MIMO receiver structure and showed performance for 3GPP LTE Advanced system, and verified that hard decoded bits, PIC-MMSE (Parallel Interference Cancellation - MMSE) and 1-iteration PIC are enough to meet the required SNR using the implemented test-bed system.

Due to their computational complexity, iterative decoder components such as source and channel decoders are usually implemented using specialised dedicated hardware. This leads to the scenario where each different iterative decoder component of the receiver requires its own hardware. Due to their relatively high complexity, many capacity‐approaching techniques proposed in the literature have not yet been invoked in wireless sensor network applications, despite their potential benefits of facilitating a reduced transmission power or extended communication range. Against this background, the authors propose an energy‐efficient architecture comprised of multiple computation unit (CU), which is sufficiently flexible for accommodating different iterative decoder components using the same hardware. In this study, the flexible architecture is applied to Joint Source and Channel Coding (JSCC), comprising the Unary Error Correction (UEC) code, a turbo code and an iterative demodulator. The authors conceive a flexible technique for controlling the hardware, which supports a high hardware‐exploitation ratio for the CU, reaching a utilisation of 88%, compared with 68% achieved in similar solutions reported in the open literature.

This paper applies the expectation-maximization (EM) algorithm to address the problem of joint channel estimation and channel decoding in Physical-layer Network Coding (PNC) systems. The use of PNC can significantly improve the throughput of a relay network. The throughput advantage, however, is predicated on the availability of accurate channel estimates. For channel-coded PNC systems, a major challenge is that the maximum a posteriori probability (MAP) channel estimation is nontrivial due to 1) the overlapping of signals from multiple users received at the relay; and 2) the correlations among data symbols introduced by channel coding. In this paper, we show that an EM algorithm implemented on a factor graph framework is well suited to tackle this problem. Through iterative message passing, the channel estimation component and the channel decoding component in the factor graph interact to improve each other's results progressively. Simulation results indicate that just one EM iteration of our algorithm can significantly improve the channel estimation accuracy as well as the BER performance of channel-coded PNC systems.

A low-complexity Multiple Input Multiple Output receiver based on Belief Propagation (MIMO-BP) detector associated with a Non-Binary Low Density Parity Check (NB-LDPC) decoder is proposed in this paper. Such detection and decoding algorithms are represented thanks to a larger Joint Factor Graph. Shuffle schedule is also applied to efficiently exchange information between the detector and the decoder. Actions are undertaken at the detector, decoder and the iterative receiver levels in order to reduce overall computational complexity. An important reduction in terms of operations per iteration is obtained with a negligible performance penalty. Then, EXtrinsic Information Transfer (EXIT) charts are used to find a schedule in terms of number of iterations to be performed for which the proposed receiver achieves error correction performance similar to that of a full-complexity iterative MIMO receiver.

In this paper we propose a novel turbo equalizer based on the expectation propagation (EP) algorithm. Optimal equalization is computationally unfeasible when high-order modulations and/or large memory channels are used. In these scenarios, low-cost and suboptimal equalizers, such as those based on the linear minimum mean square error (LMMSE), are commonly used. The LMMSE-based equalizer can be efficiently implemented with a Kalman smoother (KS), i.e., a forward and backward Kalman filtering whose predictions are merged in a posterior smoothing step. Recently, it was shown that applying EP at the forward and backward stages of a KS equalizer could significantly improve its performance. In this paper, we investigate applying EP at the smoothing level instead. Also, we propose some further modifications to better exploit the information coming from the channel decoder in turbo equalization schemes. Overall, we remarkably reduce the computational complexity while highly improving the performance in terms of bit error rate.

The spectrum resource in the air becomes more occupied, so it is necessary to increase spectrum efficiency with our purpose to relax more resource. Traditionally, we increase the data rate in the unit frequency band through higher order modulation, orthogonal frequency division multiplexing (OFDM) and pulse shaping. However, another way for improving spectrum efficiency is to reduce the transmitting bandwidth required for unit data rate. From this point of view, the ultra narrow band (UNB) modulation was proposed. UNB modulation is a sort of transmission technique with efficient bandwidth and high data rate. This patent review will highlight recent patents on UNB modulations, and briefly explain their theoretic foundation and key technology. Keywords: Ultra narrow band (UNB), high efficient modulations, high speed communications, special filters, modulator and demodulator (MODEM), channel coding, wireless communications, orthogonal frequency division multiplexing, Bandwidth, Sinusoidal Carrier, Carrier Energy, Waveform Manipulation Technologies, amplitude modulation, signal bandwidth, Geometric Feature Filters, Wavelet Pass Filters

We consider differential MIMO architecture based on Cayley transform (Hassibi and Hochwald, 2002) when the channel state information is available neither at the transmitter nor the receiver. Within this framework, a low complexity iterative receiver is proposed based on a linearly complex space matched filtering signal detection, iterative probabilistic data association (PDA) and turbo equalization. Simulation results on Rayleigh faded MIMO channels illustrate good performances of the designed receiver.

Orthogonal frequency division multiplexing (OFDM) systems suffer performance degradation in fast fading channels due to intercarrier interference (ICI). Combining frequency domain equalization and bit-interleaved coded modulation (BICM), the iterative receiver is able to harvest both temporal and frequency diversity. In order to perform coherent detection and estimation, channel state information (CSI) is indispensable. Being able to accurately and effectively acquire CSI in the fast time-varying channel is critical. Conventional frequency domain channel estimation (CE) methods have an irreducible error floor at high normalized Doppler frequency fdTs, since ICI corrupts the orthogonality among subcarriers. Considering that the fast time-varying channel is also a source of temporal diversity, CE ought to take place in the pre-FFT time domain. Realizing channel variations in time are often smooth, this paper proposes a time domain channel estimator using pilot symbol assisted modulation (PSAM) with complexity O(N), where N is the FFT size. Simulation results demonstrate that a PSAM system with channel estimation provides excellent trade-off between performance, complexity and spectral efficiency.

Orthogonal frequency division multiplexing (OFDM) system suffers extra performance degradation in fast fading channels due to intercarrier interference (ICI). Combining frequency domain equalization and bit-interleaved coded modulation (BICM), the iterative receiver is able to harvest both temporal and frequency diversity. Realizing that ICI channels are intrinsically ISI channels, this paper proposes a soft-in soft-out (SISO) maximum a posteriori (MAP) equalizer by extending Ungerboeck's maximum likelihood sequence estimator (MLSE) formulation to ICI channels. The SISO MAP equalizer employs BCJR algorithm and computes the bit log-likelihood ratios (LLR) for the entire received sequence by efficiently constructing a trellis that takes into account of the ICI channel structure. A reduced state (RS) formulation of the SISO MAP equalizer which provides good performance/complexity tradeoff is also described. Utilizing the fact that ICI energy is clustered in adjacent subcarriers, frequency domain equalization is made localized. This paper further proposes two computational efficient linear minimum mean square error (LMMSE) based equalization methods: recursive q-tap SIC-LMMSE equalizer and recursive Sliding-Window (SW) SIC-LMMSE equalizer respectively. Simulations results demonstrate that the iterative SISO RS-MAP equalizer achieves the performance of no ICI with normalized Doppler frequency f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> up to 20.46% in realistic mobile WiMAX environment.