Showing papers by "Amine Mezghani published in 2008"

Analysis of Rayleigh-fading channels with 1-bit quantized output

Abstract: We consider multi-input multi-output (MIMO) Rayleigh-fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication with low cost devices. The key point is that the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We elaborate on some properties of the mutual information compared to the unquantized case. For the SISO case, we show that on-off QPSK signaling is the capacity achieving distribution. To our knowledge, the block-wise Rayleigh-fading channel with mono-bit detection was not studied in the literature.

Maximum likelihood detection for quantized MIMO systems

Abstract: In this work, maximum likelihood-detection (ML-detection) for MIMO systems operating on quantized data is considered. It turns out that the optimal decision rule is generally intractable. Therefore, we propose a suboptimal solution with lower complexity. Assuming a Rayleigh fading MIMO environment, an upper bound on the error probability is derived for the case where the number of transmit and receive antennas are equal. Furthermore, we introduce a new performance measure that relates the outage properties to the bit resolution in this context.

Spatial MIMO decision feedback equalizer operating on quantized data

Abstract: We study the joint optimization of the quantizer and the spatial decision feedback equalizer (DFE) for the flat multi-input multi-output (MIMO) channel with quantized outputs. Our design is based on a minimum mean square error (MMSE) approach, taking into account the effects of quantization. Our derivation does not make use of the assumption of uncorrelated white quantization errors and considers the correlations of the quantization error with the other signals of the system. Through simulation, we compare the new DFE to the conventional spatial DFE operating on quantized data in terms of uncoded BER.

On maximizing the sum network MISO broadcast capacity

Abstract: Recently, it has been shown that dirty paper coding (DPC) achieves the sum rate capacity of the Gaussian multi-user multiple-input single-output (MU-MISO) broadcast channel of a single isolated cell. However, when considering a multi- cell scenario, i.e., a cellular network, the optimal strategy to maximize the sum rate capacity in each of the cells is still unknown. Nevertheless, based on a game-theoretic framework DPC can be applied at each cell as a decentralized strategy in a cellular network, in order to maximize the sum broadcast capacity of the network. By treating the cells in the network as players in a strategic cooperative game, simultaneous iterative waterfilling can be performed, i.e., every cell computes its optimal beamforming vectors according to DPC and by considering the intercell interference generated in the previous iteration. At each iteration the beamforming vectors for each user in each cell are updated with the gradient projection algorithm in order to maximize the sum network broadcast capacity. The algorithm is repeated until it converges, i.e., a local maximum is achieved. This theoretic result approaches the maximum rate that can be transmitted in the downlink of a network. Additionally, in order to introduce some fairness into the network, we consider in a similar way as the previous problem, the task of minimizing the sum of the mean square errors of all the users in the network.

Achieving near-capacity on large discrete memoryless channels with uniform distributed selected input

Abstract: We propose a method to increase the capacity achieved by uniform prior in discrete memoryless channels (DMC) with high input cardinality. It consists in appropriately reducing the input set. Different design criteria of the input subset are discussed. We develop an efficient algorithm to solve this problem based on the maximization of the cut-off rate. The method is applied to a mono-bit transceiver MIMO system, and it is shown that the capacity can be approached within tenths of a dB by employing standard binary codes while avoiding the use of distribution shapers.