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Spatial–temporal (ST) subspace-based channel estimation techniques formulated with $\ell 2$ minimum mean square error (MMSE) criterion alleviate the multi-access interference (MAI) problem when the interested signals exhibit low-rank property. However, the conventional $\ell 2$ ST subspace-based methods suffer from mean squared error (MSE) deterioration in unknown interference channels, due to the difficulty to separate the interested signals from the channel covariance matrices (CCMs) contaminated with unknown interference. As a solution to the problem, we propose a new $\ell 1$ regularized ST channel estimation algorithm by applying the expectation-maximization (EM) algorithm to iteratively examine the signal subspace and the corresponding sparse-supports. The new algorithm updates the CCM independently of the slot-dependent $\ell 1$ regularization, which enables it to correctly perform the sparse-independent component analysis (ICA) with a reasonable complexity order. Simulation results shown in this paper verify that the proposed technique significantly improves MSE performance in unknown interference MIMO channels, and hence, solves the BER floor problems from which the conventional receivers suffer. [ABSTRACT FROM AUTHOR]

For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations. [ABSTRACT FROM AUTHOR]

Inter-block-interference (IBI) caused in the pursuit of spectral efficiency can deteriorate channel estimation performance. For this problem, previously-proposed chained turbo estimation performs IBI cancelation by using the soft replica of the transmitted signal. The IBI cancelation technique can, however, suffer from a mean squared error (MSE) floor problem, since the soft replica is unavailable at the first turbo iteration. The IBI problem can be avoided by using channel impulse response length constraint. Nevertheless, as shown in this paper, the IBI avoidance approach is difficult to perform independently since it requires unbiased second-order statistics. This paper proposes, therefore, a new conditional $\ell 1$ regularized minimum mean square error channel estimation algorithm by jointly utilizing the IBI avoidance/cancelation and subspace techniques. Simulation results verify that the proposed algorithm solves the MSE floor problem, and, hence, improves the bit error rate convergence performance in realistic IBI channels including the effect of pulse shaping filters. [ABSTRACT FROM AUTHOR]

Turbo channel estimation algorithms can improve estimation accuracy in multiuser (MU) multi-input multi-output (MIMO) systems. However, the conventional least squares (LS) turbo channel estimation technique based on the maximum likelihood approach is difficult to be performed in large MU-MIMO systems since it requires a cubic complexity order $\mathcal {O}(\kappa ^3 N_T^3 N_R^3)$ for a system constant $\kappa$ and numbers of $N_T$ -transmit and $N_R$ -receive antennas. This is because the conventional technique computes inversion of a covariance matrix whose size is proportional to $\kappa N_T N_R$. We propose a new low-complexity LS estimation technique that requires a complexity order $\mathcal {O}(\kappa ^3 N_T^3)$ only by utilizing algebraic property on the covariance matrix. Simulation results shown in this letter verify that the proposed technique significantly reduces processing time without sacrificing the estimation performance. [ABSTRACT FROM AUTHOR]

This paper considers noisy Wyner–Ziv coding (WZC), in which a remote noisy source is compressed with side information at the decoder only. The decoder side information is not limited to being Gaussian, thus this noisy WZC problem cannot be transformed into the conventional one tailored for noiseless sources. A new coding structure, named the residual-quantization (RQ) based noisy WZC, is proposed to solve the problem. This scheme explicitly constructs the theoretical auxiliary random variable to facilitate optimal reconstruction of the noisy source. In this two-stage encoder, the noisy source is quantized twice and the quantization error (residue) of the first stage is the input of the second stage. By sending only the quantization index of the second stage to the decoder, the corresponding code rate can theoretically approach the noisy WZC bound. Moreover, the RQ-based noisy WZC is implemented using graph-based codes. The main challenge is that it is necessary to design a codebook that is simultaneously good for source and channel coding for the first stage quantization, since this quantization code also acts as a channel code at the decoder. This problem is solved by constructing a low-density parity check (LDPC) code with edge degrees optimized for channel coding, and enhancing its performance for source coding by using a modified reinforced belief-propagation quantization algorithm. Simulation results show that the noisy Wyner-Ziv bounds can be practically approached by our implementation. In addition, the proposed implementation offers more flexibility in the code rates compared with the existing practical designs, making it more suitable for emerging applications such as fronthaul compression. [ABSTRACT FROM AUTHOR]

The computational complexity and peak-to-average power ratio (PAPR) of conventional precoded orthogonal frequency division multiplexing (OFDM) systems can be reduced using a T-OFDM precoded system based on the Walsh–Hadamard matrix. The present paper proposes a novel precoding scheme for further reducing the computational complexity and PAPR of T-OFDM. In the proposed scheme, the precoding matrix is combined with an inverse discrete Fourier transform to construct a new transform matrix at the transmitter. Notably, the transform matrix is both unitary and circulant, with each column being a perfect Gaussian integer sequence containing just four non-zero elements of \\pm 1,\pm j\. A low-complexity receiver is additionally constructed for the proposed precoding scheme. A closed-form expression is derived for the bit error rate (BER) in T-OFDM and the proposed precoding scheme under frequency-selective fading channels. The simulation results for the BER are shown to be in good agreement with the mathematical derivations. In addition, it is demonstrated that T-OFDM and the proposed scheme have an equivalent BER performance when their precoding matrices are designed in such a way as to obtain full frequency diversity. However, the proposed scheme has a better PAPR performance and a lower computational complexity than T-OFDM. [ABSTRACT FROM PUBLISHER]