Guillaume Andrieux

Channel time variation effects on transmit beamforming in the TDD mode of UMTS

In cellular mobile radio systems, transmit beamforming can be used to reduce the interference in the downlink. In a time division duplex (TDD) system with sufficiently short duplex period, the uplink channel estimation can be used to design the downlink beamformer. The purpose of this paper is to analyse the sensitivity of several transmit techniques to the channel variations between uplink and downlink time-slots. This analysis is done using experimental data obtained from real environments.

Threshold based most significant taps detection for sparse channel estimation in OFDM system

This paper addresses the effective sparse channel estimation based on the most significant taps (MST) selection with high spectral efficiency and moderate computational complexity for orthogonal frequency division multiplexing (OFDM) systems. The main goal of this paper is to obtain an efficient time domain threshold and achieve the channel estimation performance approaching the least squares (LS) method with exact sparsity (exact number of non-zero channel taps) without using prior knowledge of both channel statistics and noise standard deviation. Firstly, classical LS method is used to get an initial channel impulse response (CIR) estimate. Then a novel effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Simulations show that the proposed method is able to achieve good channel estimation performances for sparse channel within a comparatively wide range of sparsity with small number of pilots and moderate computational complexity.

A novel effective compressed sensing based sparse channel estimation in OFDM system

The optimal tradeoff among the channel estimation performance, spectrum efficiency and computational complexity has always been one of the major topics for wireless communication system especially OFDM system. Traditional channel estimation methods are usually based on the classical (Least Squares) LS method without considering whether the multipath channel is sparse or rich, therefore, those methods can hardly balance the channel estimation performance, spectrum efficiency and computational complexity. In this paper, we propose an effective compressed sensing (CS) based sparse channel estimation method for OFDM system. As an efficient reconstruction tool for CS, the orthogonal matching pursuit (OMP) is adopted in this paper for sparse channel estimation. For OMP algorithm, an good threshold is essential to promote the channel estimation accuracy. The proposed threshold is formulated based on theoretical derivation and analysis. Both simulation results and computational complexity evaluation show that the proposed method can effectively balance the channel estimation performance, spectrum efficiency and computational complexity.

Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN

Energy efficiency is the key requirement to maximize sensor node lifetime. Sensor nodes are typically powered by a battery source that has finite lifetime. Most Internet of Thing (IoT) applications require sensor nodes to operate reliably for an extended period of time. To design an autonomous sensor node, it is important to model its energy consumption for different tasks. Each task consumes a power consumption amount for a period of time. To optimize the consumed energy of the sensor node and have long communication range, Low Power Wide Area Network technology is considered. This paper describes an energy consumption model based on LoRa and LoRaWAN, which allows estimating the consumed power of each sensor node element. The definition of the different node units is first introduced. Then, a full energy model for communicating sensors is proposed. This model can be used to compare different LoRaWAN modes to find the best sensor node design to achieve its energy autonomy.

A novel solar energy predictor for communicating sensors

Solar energy harvesting constitutes an attractive solution to provide energy for communicating objects. The advantage of this energy over other forms of renewable energy is that the available solar power can be predicted with reasonable accuracy, which allows the implementation of efficient power management techniques. Nevertheless, harvested solar energy comes from a non-controllable source. Therefore, the prediction of the solar radiation and energy availability is a critical issue, as the amount of the harvested energy may vary over time. In this study, a novel solar radiation and energy predictor (Solar Energy Predictor for Communicating Sensors: SEPCS) is proposed. This prediction model uses past energy observations to forecast future energy availability in short term. To assess the performance of the proposed algorithm, authors used database providing the solar radiation evolution for one year. Then, a comparative performance evaluation shows that the SEPCS predictor significantly outperforms the state-of-the-art energy predictors, by decreasing the average prediction error from 28 to 6.5%.

Rate optimization for energy efficient system with M-QAM

In this paper, we have considered the optimization of the data rate in the case of point-to-point transmission with M-ary quadrature amplitude modulation (M-QAM). The main goal is to minimize the bit energy consumption under an average bit error rate (BER) constraint in additive white gaussian noise (AWGN) and Rayleigh channel. With the evolution of wireless communication towards shorter transmission distances, the circuit energy in system hardware becomes significant in the computation of the energy expenditure as well as the transmission energy. We use an accurate power model for the circuit energy consumption. The total energy consumption is then formulated as a function of the constellation size, distance, bandwidth and other channel parameters. Optimal data rate is obtained in closed form expression and the effect of bandwidth, distance and channel path loss is studied.

Realistic Energy Consumption Model for On-Off Keying Based Minimum Energy Coding

The main objective of this paper is to present realistic energy consumption models to evaluate the energy consumption including both the transmission energy and the circuit energy at the transmitter when using Minimum Energy coding (ME-coding) On-Off Keying (OOK) scheme, we consider a simple communication link between sensor nodes. We present two energy models with a specific circuit (MAX1472) and we consider also the limitations, introduced by a practical circuit, in terms of symbol bit rate and output power. The results with the first model demonstrate that the total energy consumption of ME- Coding OOK is lower than that of the uncoded OOK, but the optimal choice of the code must take into account the limitations of the circuit. The second model with MAX1472 shutdown mode brings a reduction of the total energy consumption compared with the first model but is limited in the symbol rate. Due to this limitation, the optimal choice is finally the first model with a larger bit rate.

Interference reduction in time duplex systems by space-time beamformers

In cellular mobile radio systems, and more especially in the third-generation wireless communication systems, transmit beamformers can be used to reduce the multiple access interference (MAI) in the downlink in order to increase the quality of service and the capacity of communication systems. However, for the most conventional beamforming methods, the inter-symbol interference (ISI) or the inter-chip interference (ICI) is not taken into account, which limits the performances of these methods. In this paper, we propose a new method which uses frequency-selective beamformers to reduce jointly the MAI and the ICI. Uplink data obtained with an UMTS-TDD measurement campaign have been used to show the behaviour of the proposed method. Simulations show the efficiency of this method which improves the performances obtained with classical methods.