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Jalden J.,KTH Royal Institute of Technology | Elia P.,Eurecom
IEEE Transactions on Information Theory

In the setting of quasi-static multiple-input multiple-output channels, we consider the high signal-to-noise ratio (SNR) asymptotic complexity required by the sphere decoding (SD) algorithm for decoding a large class of full-rate linear space-time codes. With SD complexity having random fluctuations induced by the random channel, noise, and codeword realizations, the introduced SD complexity exponent manages to concisely describe the computational reserves required by the SD algorithm to achieve arbitrarily close to optimal decoding performance. Bounds and exact expressions for the SD complexity exponent are obtained for the decoding of large families of codes with arbitrary performance characteristics. For the particular example of decoding the recently introduced threaded cyclic-division-algebra-based codes - the only currently known explicit designs that are uniformly optimal with respect to the diversity multiplexing tradeoff - the SD complexity exponent is shown to take a particularly concise form as a non-monotonic function of the multiplexing gain. To date, the SD complexity exponent also describes the minimum known complexity of any decoder that can provably achieve a gap to maximum likelihood performance that vanishes in the high SNR limit. © 2012 IEEE. Source

Zakhour R.,University of Melbourne | Gesbert D.,Eurecom
IEEE Transactions on Signal Processing

This paper addresses cooperation in a multicell environment where base stations (BSs) wish to jointly serve multiple users, under a constrained-capacity backhaul. We point out that for finite backhaul capacity a tradeoff between sharing user data, which allows for full MIMO cooperation, and not doing so, which reduces the setup to an interference channel but also requires less overhead, emerges. We optimize this tradeoff by formulating a rate splitting approach in which non-shared data (private to each transmitter) and shared data are superimposed. We derive the corresponding achievable rate region and obtain the optimal beamforming design for both shared and private symbols. We illustrate how the capacity of the backhaul determines how much of the user data is worth sharing across multiple BSs. © 2011 IEEE. Source

Wang L.,Eurecom | Kuo G.-S.G.S.,National Chengchi University
IEEE Communications Surveys and Tutorials

In heterogeneous wireless networks, an important task for mobile terminals is to select the best network for various communications at any time anywhere, usually called network selection. In recent years, this topic has been widely studied by using various mathematical theories. The employed theory decides the objective of optimization, complexity and performance, so it is a must to understand the potential mathematical theories and choose the appropriate one for obtaining the best result. Therefore, this paper systematically studies the most important mathematical theories used for modeling the network selection problem in the literature. With a carefully designed unified scenario, we compare the schemes of various mathematical theories and discuss the ways to benefit from combining multiple of them together. Furthermore, an integrated scheme using multiple attribute decision making as the core of the selection procedure is proposed. © 1998-2012 IEEE. Source

Jalden J.,KTH Royal Institute of Technology | Elia P.,Eurecom
IEEE Transactions on Information Theory

This paper identifies the first general, explicit, and nonrandom MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statistics, for all channel dimensions, and most interestingly, irrespective of the particular lattice-code applied. As a special case, it is established that the LLL-based LR-aided linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal decoding of any lattice code at a worst-case complexity that grows at most linearly in the data rate. This represents a fundamental reduction in the decoding complexity when compared to ML decoding whose complexity is generally exponential in the rate. The results' generality lends them applicable to a plethora of pertinent communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI, cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality of the LR-aided linear decoder is guaranteed. The adopted approach yields insight, and motivates further study, into joint transceiver designs with an improved SNR gap to ML decoding. © 2010 IEEE. Source

Couillet R.,Eurecom | Debbah M.,Ecole Normale Superieure de Cachan
IEEE Signal Processing Magazine

An accessible methodological introduction to the modern tools of random matrix theory is presented and the signal processing methods derived from them are discussed. Many traditional signal processing methods are inconsistent when both the population and system dimensions are large. Notions of random matrix theory are introduced that provide results on the spectrum of large random matrices. These results are then used to adjust some of these inconsistent signal processing methods to new consistent estimates. To this end, a recent method based on the Stieltjes transform is presented to derive weak convergence properties of the spectrum of large matrices and on complex integration to derive estimators. This somewhat parallels the Fourier transforms and M-estimator framework usually met in classical asymptotic signal processing. Source

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