On the relaxed maximum-likelihood blind MIMO channel estimation for orthogonal space-time block codes

This paper concerns the maximum-likelihood channel estimation for MIMO systems with orthogonal space-time block codes when the finite alphabet constraint of the signal constellation is relaxed. We study the channel coefficients estimation subspace generated by this method. We provide an algebraic characterisation of this subspace which turns the optimization problem into a purely algebraic one and more importantly, leads to several interesting analytical proofs. We prove that with probability one, the dimension of the estimation subspace for the channel coefficients is deterministic and it decreases by increasing the number of receive antennas up to a certain critical number of receive antennas, after which the dimension remains constant. In fact, we show that beyond this critical number of receive antennas, the estimation subspace for the channel coefficients is isometric to a fixed deterministic invariant space which can be easily computed for every specific OSTB code

Low-complexity blind maximum-likelihood detection for SIMO systems with general constellations

The demand for high data rate reliable communications poses great challenges to the next generation wireless systems in highly dynamic mobile environments. In this paper, we investigate the joint maximum-likelihood (ML) channel estimation and signal detection problem for single-input multiple-output (SIMO) wireless systems with general modulation constellations and propose an efficient sequential decoder for finding the exact joint ML solution. Unlike other known methods, the new decoder can even efficiently find the joint ML solution under high spectral efficiency non-constant- modulus modulation constellations. In particular, the new algorithm does not need such preprocessing steps as Cholesky or QR decomposition in the traditional sphere decoders for joint ML channel estimation and data detection. The elimination of such preprocessing not only reduces the number of floating point computations, but also will potentially lead to smaller size and power consumption in VLSI implementations while providing better numerical stability

Coded DS-CDMA Systems with Iterative Channel Estimation and no Pilot Symbols

In this paper, we describe direct-sequence code-division multiple-access (DS-CDMA) systems with quadriphase-shift keying in which channel estimation, coherent demodulation, and decoding are iteratively performed without the use of any training or pilot symbols. An expectation-maximization channel-estimation algorithm for the fading amplitude, phase, and the interference power spectral density (PSD) due to the combined interference and thermal noise is proposed for DS-CDMA systems with irregular repeat-accumulate codes. After initial estimates of the fading amplitude, phase, and interference PSD are obtained from the received symbols, subsequent values of these parameters are iteratively updated by using the soft feedback from the channel decoder. The updated estimates are combined with the received symbols and iteratively passed to the decoder. The elimination of pilot symbols simplifies the system design and allows either an enhanced information throughput, an improved bit error rate, or greater spectral efficiency. The interference-PSD estimation enables DS-CDMA systems to significantly suppress interference.Comment: To appear, IEEE Transactions on Wireless Communication

A new subspace method for blind estimation of selective MIMO-STBC channels

In this paper, a new technique for the blind estimation of frequency and/or time-selective multiple-input multiple-output (MIMO) channels under space-time block coding (STBC) transmissions is presented. The proposed method relies on a basis expansion model (BEM) of the MIMO channel, which reduces the number of parameters to be estimated, and includes many practical STBC-based transmission scenarios, such as STBC-orthogonal frequency division multiplexing (OFDM), space-frequency block coding (SFBC), time-reversal STBC, and time-varying STBC encoded systems. Inspired by the unconstrained blind maximum likelihood (UML) decoder, the proposed criterion is a subspace method that efficiently exploits all the information provided by the STBC structure, as well as by the reduced-rank representation of the MIMO channel. The method, which is independent of the specific signal constellation, is able to blindly recover the MIMO channel within a small number of available blocks at the receiver side. In fact, for some particular cases of interest such as orthogonal STBC-OFDM schemes, the proposed technique blindly identifies the channel using just one data block. The complexity of the proposed approach reduces to the solution of a generalized eigenvalue (GEV) problem and its computational cost is linear in the number of sub-channels. An identifiability analysis and some numerical examples illustrating the performance of the proposed algorithm are also providedThis work was supported by the Spanish Government under projects TEC2007-68020-C04-02/TCM (MultiMIMO) and CONSOLIDER-INGENIO 2010 CSD2008-00010 (COMONSENS)

High-Rate Space-Time Coded Large MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16x16 and 32x32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.Comment: v3: Performance/complexity comparison of the proposed scheme with other large-MIMO architectures/detectors has been added (Sec. IV-D). The paper has been accepted for publication in IEEE Journal of Selected Topics in Signal Processing (JSTSP): Spl. Iss. on Managing Complexity in Multiuser MIMO Systems. v2: Section V on Channel Estimation is update

On exact maximum-likelihood detection for non-coherent MIMO wireless systems: A branch-estimate-bound optimization framework

Fast fading wireless environments pose a great challenge for achieving high spectral efficiency in next generation wireless systems. Joint maximum-likelihood (ML) channel estimation and signal detection is of great theoretical and practical interest, especially for multiple-input multiple-output(MIMO) systems where the multiple channel coefficients need to be estimated. However, this is a hard combinatorial optimization problem, for which obtaining efficient exact algorithms has been elusive for the general MIMO systems. In this paper, we propose an efficient branch-estimate-bound non-coherent optimization framework which provably achieves the exact ML joint channel estimation and data detection for general MIMO systems. Numerical results indicate that the exact joint ML method can achieve substantial performance improvements over suboptimal methods including iterative channel estimation and signal detection. We also derive analytical bounds on the computational complexity of the new exact joint ML method and show that its average complexity approaches a constant times the length of the coherence time, as the SNR approaches infinity