Adaptive Bit Allocation With Reduced Feedback for Wireless Multicarrier Transceivers

With the increasing demand in the wireless mobile applications came a growing need to transmit information quickly and accurately, while consuming more and more bandwidth. To address this need, communication engineers started employing multicarrier modulation in their designs, which is suitable for high data rate transmission. Multicarrier modulation reduces the system's susceptibility to the frequency-selective fading channel, by transforming it into a collection of approximately flat subchannels. As a result, this makes it easier to compensate for the distortion introduced by the channel. This thesis concentrates on techniques for saving bandwidth usage when employing adaptive multicarrier modulation, where subcarrier parameters (bit and energy allocations) are modulated based on the channel state information feedback obtained from previous burst. Although bit and energy allocations can substantially increase error robustness and throughput of the system, the feedback information required at both ends of the transceiver can be large. The objective of this work is to compare different feedback compression techniques that could reduce the amount of feedback information required to perform adaptive bit and energy allocation in multicarrier transceivers. This thesis employs an approach for reducing the number of feedback transmissions by exploiting the time-correlation properties of a wireless channel and placing a threshold check on bit error rate (BER) values. Using quantization and source coding techniques, such as Huffman coding, Run length encoding and LZWalgorithms, the amount of feedback information has been compressed. These calculations have been done for different quantization levels to understand the relationship between quantization levels and system performance. These techniques have been applied to both OFDM and MIMO-OFDM systems

Adaptive OFDM Modulation for Underwater Acoustic Communications: Design Considerations and Experimental Results

Cataloged from PDF version of article.In this paper, we explore design aspects of adaptive modulation based on orthogonal frequency-division multiplexing (OFDM) for underwater acoustic (UWA) communications, and study its performance using real-time at-sea experiments. Our design criterion is to maximize the system throughput under a target average bit error rate (BER). We consider two different schemes based on the level of adaptivity: in the first scheme, only the modulation levels are adjusted while the power is allocated uniformly across the subcarriers, whereas in the second scheme, both the modulation levels and the power are adjusted adaptively. For both schemes we linearly predict the channel one travel time ahead so as to improve the performance in the presence of a long propagation delay. The system design assumes a feedback link from the receiver that is exploited in two forms: one that conveys the modulation alphabet and quantized power levels to be used for each subcarrier, and the other that conveys a quantized estimate of the sparse channel impulse response. The second approach is shown to be advantageous, as it requires significantly fewer feedback bits for the same system throughput. The effectiveness of the proposed adaptive schemes is demonstrated using computer simulations, real channel measurements recorded in shallow water off the western coast of Kauai, HI, USA, in June 2008, and real-time at-sea experiments conducted at the same location in July 2011. We note that this is the first paper that presents adaptive modulation results for UWA links with real-time at-sea experiments. © 2013 IEEE

Differential encoding techniques applied to speech signals

The increasing use of digital communication systems has produced a continuous search for efficient methods of speech encoding. This thesis describes investigations of novel differential encoding systems. Initially Linear First Order DPCM systems employing a simple delayed encoding algorithm are examined. The systems detect an overload condition in the encoder, and through a simple algorithm reduce the overload noise at the expense of some increase in the quantization (granular) noise. The signal-to-noise ratio (snr) performance of such d codec has 1 to 2 dB's advantage compared to the First Order Linear DPCM system. In order to obtain a large improvement in snr the high correlation between successive pitch periods as well as the correlation between successive samples in the voiced speech waveform is exploited. A system called "Pitch Synchronous First Order DPCM" (PSFOD) has been developed. Here the difference Sequence formed between the samples of the input sequence in the current pitch period and the samples of the stored decoded sequence from the previous pitch period are encoded. This difference sequence has a smaller dynamic range than the original input speech sequence enabling a quantizer with better resolution to be used for the same transmission bit rate. The snr is increased by 6 dB compared with the peak snr of a First Order DPCM codea. A development of the PSFOD system called a Pitch Synchronous Differential Predictive Encoding system (PSDPE) is next investigated. The principle of its operation is to predict the next sample in the voiced-speech waveform, and form the prediction error which is then subtracted from the corresponding decoded prediction error in the previous pitch period. The difference is then encoded and transmitted. The improvement in snr is approximately 8 dB compared to an ADPCM codea, when the PSDPE system uses an adaptive PCM encoder. The snr of the system increases further when the efficiency of the predictors used improve. However, the performance of a predictor in any differential system is closely related to the quantizer used. The better the quantization the more information is available to the predictor and the better the prediction of the incoming speech samples. This leads automatically to the investigation in techniques of efficient quantization. A novel adaptive quantization technique called Dynamic Ratio quantizer (DRQ) is then considered and its theory presented. The quantizer uses an adaptive non-linear element which transforms the input samples of any amplitude to samples within a defined amplitude range. A fixed uniform quantizer quantizes the transformed signal. The snr for this quantizer is almost constant over a range of input power limited in practice by the dynamia range of the adaptive non-linear element, and it is 2 to 3 dB's better than the snr of a One Word Memory adaptive quantizer. Digital computer simulation techniques have been used widely in the above investigations and provide the necessary experimental flexibility. Their use is described in the text

The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range

Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theory, commensurate reductions in the size, weight, power consumption, and/or monetary cost of both signal sensors and any associated communication links. This paper examines the use of CS in the design of a wideband radio receiver in a noisy environment. We formulate the problem statement for such a receiver and establish a reasonable set of requirements that a receiver should meet to be practically useful. We then evaluate the performance of a CS-based receiver in two ways: via a theoretical analysis of its expected performance, with a particular emphasis on noise and dynamic range, and via simulations that compare the CS receiver against the performance expected from a conventional implementation. On the one hand, we show that CS-based systems that aim to reduce the number of acquired measurements are somewhat sensitive to signal noise, exhibiting a 3dB SNR loss per octave of subsampling, which parallels the classic noise-folding phenomenon. On the other hand, we demonstrate that since they sample at a lower rate, CS-based systems can potentially attain a significantly larger dynamic range. Hence, we conclude that while a CS-based system has inherent limitations that do impose some restrictions on its potential applications, it also has attributes that make it highly desirable in a number of important practical settings

Covariance Estimation from Limited Data: State-of-the-Art, Algorithm Implementation, and Application to Wireless Communications

There are many different methods of filtering that have been known to provide better results than basic Sample-Matrix-Inversion (SMI). Providing a well-conditioned covariance matrix to these filters will provide a higher Signal to Interference plus Noise Ratio (SINR) than SMI filtering when there are large numbers of receiver element and interference signals. The available number of coherent snapshots that will be used for filter estimation is “not large enough” with respect to the array dimensionality. Common techniques to deal with this situation is to use Krylov or Singular Value Decomposition (SVD) reduced rank filtering. Auxiliary Vector (AV) filtering with the CV-MOV and J Divergence methods of choosing a termination index is improved upon by the hybrid estimation technique JMOV. When the Signal to Noise Ratio (SNR) is below 0 [dB] the signal of interest is buried in noise and estimation of the total number of signals present in a system is poor. If we fix the eigenvalues to be constant for eigenvectors that are not thought to correspond to signals, we use the eigenvector information that we have already went through the effort of performing SVD for. Sometimes the Eigenvalue Fixing (EIF) filtering produces better results than reduced rank filtering alone. The performance of the aforementioned filters when utilizing modern covariance matrix estimates is analyzed by varying parameters of the system model