Turbo channel estimation and equalisation for a superposition-based cooperative system

This study investigates the superposition-based cooperative transmission system. In this system, a key point is for the relay node to detect data transmitted from the source node. This issued was less considered in the existing literature as the channel is usually assumed to be flat fading and a priori known. In practice, however, the channel is not only a priori unknown but subject to frequency selective fading. Channel estimation is thus necessary. Of particular interest is the channel estimation at the relay node which imposes extra requirement for the system resources. The authors propose a novel turbo least-square channel estimator by exploring the superposition structure of the transmission data. The proposed channel estimator not only requires no pilot symbols but also has significantly better performance than the classic approach. The soft-in-soft-out minimum mean square error (MMSE) equaliser is also re-derived to match the superimposed data structure. Finally computer simulation results are shown to verify the proposed algorithm.

Turbo-MIMO for wireless communications

This article reviews an important class of MIMO wireless communications, known collectively as turbo-MIMO systems. A distinctive property of turbo-MIMO wireless communication systems is that they can attain a channel capacity close to the Shannon limit and do so in a computationally manageable manner. The article focuses attention on a subclass of turbo-MIMO systems that use space-time coding based on bit-interleaved coded modulation. Different computationally manageable decoding (detection) strategies are briefly discussed. The article also includes computer experiments that are intended to improve the understanding of specific issues involved in the design of turbo-MIMO systems.

Turbo-BLAST: Performance evaluation in correlated Rayleigh-Fading environment

Recent theoretical investigations of spatially correlated multitransmit and multireceive (MTMR) links show that not only independently and identically distributed links, but also spatially correlated links can offer linear capacity growth with increasing number of transmit and receive antennas. In this paper, we explore the suitability of the turbo-BLAST architecture in correlated Rayleigh-fading MTMR environments. In particular, for an MTMR system with a large number of receive antennas, a near optimal performance can be achieved by the turbo-BLAST architecture in spatially and temporarily correlated Rayleigh-fading environments. The performance of turbo-BLAST, in terms of both bit-error rate and spectral efficiency, is analyzed empirically in indoors and correlated outdoor environments.

Turbo-smt: Accelerating coupled sparse matrix-tensor factorizations by 200x

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ‘edible’, ‘fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem.

Can we accelerate any CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, by up to 200x, along with an up to 65 fold increase in sparsity, with comparable accuracy to the baseline.

We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy.

On the performance of LDPC and turbo decoder architectures with unreliable memories

In this paper, we investigate the impact of faulty memory bit-cells on the performance of LDPC and Turbo channel decoders based on realistic memory failure models. Our study investigates the inherent error resilience of such codes to potential memory faults affecting the decoding process. We develop two mitigation mechanisms that reduce the impact of memory faults rather than correcting every single error. We show how protection of only few bit-cells is sufficient to deal with high defect rates. In addition, we show how the use of repair-iterations specifically helps mitigating the impact of faults that occur inside the decoder itself.