Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

International audienceThis paper describes a new blind channel estimation algorithm for Space-Time Block Coded (STBC) systems. The proposed method exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after Zero-Forcing equalization. In contrast to subspace or Second-Order Statistics (SOS) approaches, the proposed method is more general since it can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBCs. Furthermore, unlike other approaches, the method does not require any modification of the transmitter and, consequently, is well-suited for non-cooperative context. Numerical examples corroborate the performance of the proposed algorithm

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Multiuser detection in CDMA using blind techniques

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2004Includes bibliographical references (leaves: 63-65)Text in English; Abstract: Turkish and Englishxiv, 69 leavesIn code division multiple access (CDMA) systems, blind multiuser detection (MUD) techniques are of great importance, especially for downlinks, since in practice, it may be unrealistic for a mobile user to know the spreading codes of other active users in the channel. Furthermore, blind methods remove the need for training sequences which leads to a gain in the channel bandwidth. Subspace concept in blind MUD is an alternative process to classical and batch blind MUD techniques based on principle component analysis, or independent component analysis (ICA) and ICA-like algorithms, such as joint approximate diagonalization of eigen-matrices (JADE), blind source separation algorithm with reference system, etc. Briefly, the desired signal is searched in the signal subspace instead of the whole space, in this type of detectors. A variation of the subspace-based MUD is reduced-rank MUD in which a smaller subspace of the signal subspace is tracked where the desired signal is contained in. This latter method leads to a performance gain compared to a standard subspace method. In this thesis, blind signal subspace and reduced-rank MUD techniques are investigated, and applied to minimum mean square error (MMSE) detectors with two different iterative subspace tracking algorithms. The performances of these detectors are compared in different scenarios for additive white Gaussian noise and for multipath fading channels as well. With simulation results the superiority of the reduced-rank detector to the signal subspace detector is shown. Additionally, as a new remark for both detectors, it is shown that, using minimum description length criterion in subspace tracking algorithm results in an increase in rank-tracking ability and correspondingly in the final performance. Finally, the performances of these two detectors are compared with MMSE, adaptive MMSE and JADE detectors