Information Theory and Reliable Communication

The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.

Audio-based context recognition

In a closed-loop wireless communication system, a codebook-based feedback mechanism is provided to enable non-unitary precoding for multi-stream transmission, where in each stream is optimized with suitable transmission power allocation and AMC. The codebook-based feedback mechanism uses a precoding codebook having a power allocation matrix which is constrained to specify that beamforming always applies full power to a predetermined beam. With this constraint, a one-bit power allocation feedback index may be used to switch between beamforming and spatial multiplexing.

MIMO precoding enabling spatial multiplexing, power allocation and adaptive modulation and coding

A multi-user MIMO downlink beamforming system with limited feedback ( 200 ) is provided to enable precoding for multi-stream transmission, where a channel codeword (u i ) and one or more channel quality indicator values (CQI A , CQI B ) are computed at the user equipment ( 201. i ) on the basis of maximizing a predetermined SINR performance metric (ρ i ) which estimates the receive signal-to-noise-ratio (SINR) at the user equipment ( 201. i ). The computed codeword (u i ) and CQI values (or differential values related thereto) are quantized and fed back to help the base station ( 210 ) which applies a correction to the appropriate CQI value in the course of designing the transmit beamforming vectors w and determining the appropriate modulation and coding level to be used for downlink data transmission.

Multi-user MIMO-SDMA for finite rate feedback systems

Distributed communications systems providing and supporting radio frequency (RF) communication services and digital data services, and related components and methods are disclosed. The RF communication services can be distributed over optical fiber to client devices, such as remote units for example. Power can also be distributed over electrical medium that is provided to distribute digital data services, if desired, to provide power to remote communications devices and/or client devices coupled to the remote communications devices for operation. In this manner, as an example, the same electrical medium used to transport digital data signals in the distributed antenna system can also be employed to provide power to the remote communications devices and/or client devices coupled to the remote communications devices. Power may be injected and switched from two or more power sources over selected electrical medium to distribute power for power-consuming components supporting RF communications services and digital data services.

Providing digital data services as electrical signals and radio-frequency (RF) communications over optical fiber in distributed communications systems, and related components and methods

A novel and useful apparatus for and method of Gaussian Minimum Shift Keying (GMSK) single antenna interference cancellation (SAIC) for use in a digital receiver. An interference mitigation module treats the problem of GMSK SAIC in a blind manner and is operative to compensate for the co-channel interference added in the communications channel which is subject to multipath propagation and fading, receiver filter and any pre-channel estimation filtering. The interference mitigation module takes advantage of the spatial diversity making up multiple branches of the received signal. The branches comprise the in-phase and quadrature elements of the received signal, the sampling phases if over sampling is applied and/or multiple antennas. The invention utilizes the spatial diversity of these multiple representations of the received signal and combines (i.e. collapses) the information in the plurality of branches into a single branch that is input to the equalizer.

Blind interference mitigation in a digital receiver

A novel and useful apparatus for and method of multiple input multiple output (MIMO) detection for use in MIMO based communication systems. The mechanism of the invention performs a simplified tree search utilizing a single stage expansion of the most likely first symbol candidates, in the case of a 2×2 MIMO system. The invention also provides a refinement mechanism that is operative to significantly improve the soft information (i.e. log likelihood ratio) of the list of candidates. To improve the soft information, the mechanism applies one or more refinements rounds to generate additional candidates for both first and second detected symbols.

Multiple-input multiple-output (mimo) detector incorporating efficient signal point search and soft information refinement

We introduce a discriminative training algorithm for the estimation of hidden Markov model (HMM) parameters. This algorithm is based on an approximation of the maximum mutual information (MMI) objective function and its maximization in a technique similar to the expectation-maximization (EM) algorithm. The algorithm is implemented by a simple modification of the standard Baum-Welch algorithm, and can be applied to speech recognition as well as to word-spotting systems. Three tasks were tested: isolated digit recognition in a noisy environment, connected digit recognition in a noisy environment and word-spotting. In all tasks a significant improvement over maximum likelihood (ML) estimation was observed. We also compared the new algorithm to the commonly used extended Baum-Welch MMI algorithm. In our tests the algorithm showed advantages in terms of both performance and computational complexity.

A discriminative training algorithm for hidden Markov models

We study the problem of communication over an additive white Gaussian noise (AWGN) channel with an AWGN feedback channel. When the feedback channel is noiseless, the classic Schalkwijk–Kailath (S-K) scheme is known to achieve capacity in a simple sequential fashion, while attaining reliability superior to non-feedback schemes. In this paper, we show how simplicity and reliability can be attained even when the feedback is noisy, provided that the feedback channel is sufficiently better than the feedforward channel. Specifically, we introduce a low-complexity low-delay interactive scheme that operates close to capacity for a fixed bit error probability (e.g., $10^{-6}$ ). We then build on this scheme to provide two asymptotic constructions, one based on high dimensional lattices, and the other based on concatenated coding, that admit an error exponent significantly exceeding the best possible non-feedback exponent. Our approach is based on the interpretation of feedback transmission as a side-information problem, and employs an interactive modulo-lattice solution.

Interactive Schemes for the AWGN Channel with Noisy Feedback

A novel and useful apparatus for and method of multiple input multiple output (MIMO) detection for use in MIMO based communication systems. The mechanism of the invention performs a simplified tree search utilizing a single stage expansion of the most likely first symbol candidates, in the case of a 2×2 MIMO system. The invention also provides a refinement mechanism that is operative to significantly improve the log likelihood (LLR) of the list of candidates. To improve the LLR, the mechanism applies refinements rounds to generate additional candidates for both first and second detected symbols.

Assaf Ben-Yishai

Papers

Blind interference mitigation in a digital receiver

Multiple-input multiple-output (mimo) detector incorporating efficient signal point search and soft information refinement

A discriminative training algorithm for hidden Markov models

Interactive Schemes for the AWGN Channel with Noisy Feedback

Multiple-input multiple-output (mimo) detector incorporating efficient signal point search