In the present paper, a semi-blind receiver for a multiuser uplink DS-CDMA (Direct-Sequence Code-Division Multiple-Access) system based on relay aided cooperative communications is proposed. For the received signal, a quadrilinear Parallel Factor (PARAFAC) tensor decomposition is adopted, such that the proposed receiver can semi-blindly estimate the transmitted symbols, channel gains and spatial signatures of all users. The estimation is done by fitting the tensor model using the Alternating Least Squares (ALS) algorithm. With computational simulations, we provide the performance evaluation of the proposed receiver for various scenarios.
In this paper, it is proposed a relay activation method for a multiuser cooperative uplink system, based on the current Signal-to-Noise Ratio (SNR) of the link between relay and base station. Depending on this current SNR, extra relays can be activated, enhancing the quality of the received signal and making the uplink transmission less susceptible to unpredictable SNR variations. The communication system is modeled as a PARAllel FACtor (PARAFAC) tensor decomposition, exploiting its uniqueness properties to estimate the transmitted symbols, channel gains and spatial signatures of the users. The proposed method is based on the iterative algorithm Alternating Least Squares (ALS). Since the receiver can estimate the channel gains, a real-time change of the number of relays would not deteriorate the channel's coefficients estimations. Computer simulations based on Monte Carlo runs show the performance of the proposed relay activation method.
This work proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the Multilinear Principal Component Analysis (MPCA) and the Support Tensor Machine (STuM). By exploiting the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations, the tensor patterns are constructed as: stations × channels × features. The multidimensional structure of the data is then preserved, avoiding the tensor vectorization that often leads to a feature vector with a large dimension, which increases the number of parameters and may cause the "curse of dimensionality". Moreover, the array vectorization breaks down the multidimensional structure of the data, which usually leads to performance degradation. The results showed a good performance of the proposed multilinear classification system, significantly outperforming its vectorial counterparts. The best result was obtained with the STuM classifier along with the MPCA.
A wide variety of packet classification algorithms exist in the research literature and commercial market. The existing solutions exploit various design tradeoffs, providing high search rates, power and space efficiency and the ability to scale to large numbers of filters. However, still remains a need for techniques that achieve a favorable balance among these tradeoffs and scale to support classification. Based on this motivations, this paper presents a tensor approach for the classification of TCP and UDP packets. By using a multidimensional structure, more specifically a 4-th order tensor, to store the packet data, a tensorial algorithm known as Support Tensor Machines (STM) is used to perform classification. Results showed good performance of the approach in comparison to other classifiers such as the Support Vector Machines and Naive-Bayes.
In this paper, four tensor-based receivers for a multiuser multirelay cooperative uplink are proposed, with the relays employ the amplify-and-forward (AF) protocol and a time-spread coding. Two different scenarios are considered regarding the multiuser interference at the relays. When multiuser interference at the relays is ignored, a quadrilinear PARAFAC model is adopted for the received signals. Otherwise, a new tensor model called Nested PARAFAC-Tucker decomposition (NPT1D) is used to represent the received signals. The proposed receivers jointly estimate the transmitted symbols, channel gains and spatial signatures, two of them being based on the Alternating Least Squares (ALS) algorithm and two of them using the non-iterative Least Squares Khatri-Rao Factorization (LS-KRF) method. Uniqueness is discussed and simulation results are provided to illustrate the performance of the proposed techniques.
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