Energy Management Policies for Energy-Neutral Source-Channel Coding

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a maximal average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and delay is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.Comment: Submitted to IEEE Transactions on Communications in March 2011; last update in July 201

Time- and Frequency-Varying KK-Factor of Non-Stationary Vehicular Channels for Safety Relevant Scenarios

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician $K$-factor. We analyze the time-frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS). This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Ricean distributed with a varying $K$-factor. The later delay bins are Rayleigh distributed. We demonstrate that the $K$-factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bi-modal Gaussian mixture model, that allows to capture the $K$-factor variability in time for safety-relevant ITS scenarios.Comment: 26 pages, 12 figures, submitted to IEEE Transactions on Intelligent Transportation Systems for possible publicatio

In-vehicle channel sounding in the 5.8-GHz band

The article reports vehicular channel measurements in the frequency band of 5.8 GHz for IEEE 802.11p standard. Experiments for both intra-vehicle and out-of-vehicle environments were carried out. It was observed that the large-scale variations (LSVs) of the power delay profiles (PDPs) can be best described through a two-term exponential decay model, in contrast to the linear models which are suitable for popular ultra-wideband (UWB) systems operating in the 3- to 11-GHz band. The small-scale variations (SSVs) are separated from the PDP by subtracting the LSV and characterized utilizing logistic, generalized extreme value (GEV), and normal distributions. Two sample Kolmogorov-Smirnov (K-S) tests validated that the logistic distribution is optimal for in-car, whereas the GEV distribution serves better for out-of-car measurements. For each measurement, the LSV trend was used to construct the respective channel impulse response (CIR), i.e., tap gains at different delays. Next, the CIR information is fed to an 802.11p simulation testbed to evaluate the bit error rate (BER) performance, following a Rician model. The BER results strongly vouch for the suitability of the protocol for in-car as well as out-of-car wireless applications in stationary environments.The article reports vehicular channel measurements in the frequency band of 5.8 GHz for IEEE 802.11p standard. Experiments for both intra-vehicle and out-of-vehicle environments were carried out. It was observed that the large-scale variations (LSVs) of the power delay profiles (PDPs) can be best described through a two-term exponential decay model, in contrast to the linear models which are suitable for popular ultra-wideband (UWB) systems operating in the 3- to 11-GHz band. The small-scale variations (SSVs) are separated from the PDP by subtracting the LSV and characterized utilizing logistic, generalized extreme value (GEV), and normal distributions. Two sample Kolmogorov-Smirnov (K-S) tests validated that the logistic distribution is optimal for in-car, whereas the GEV distribution serves better for out-of-car measurements. For each measurement, the LSV trend was used to construct the respective channel impulse response (CIR), i.e., tap gains at different delays. Next, the CIR information is fed to an 802.11p simulation testbed to evaluate the bit error rate (BER) performance, following a Rician model. The BER results strongly vouch for the suitability of the protocol for in-car as well as out-of-car wireless applications in stationary environments

Minimum-Energy Bandlimited Time-Variant Channel Prediction with Dynamic Subspace Selection

In current cellular communication systems the time-selective fading process is highly oversampled. We exploit this fact for time-variant flat-fading channel prediction by using dynamically selected predefined low dimensional subspaces spanned by discrete prolate spheroidal (DPS) sequences. The DPS sequences in each subspace exhibit a subspace-specific bandwidth matched to a certain Doppler frequency range. Additionally, DPS sequences are most energy concentrated in a time interval matched to the channel observation interval. Both properties enable the application of DPS sequences for minimum-energy (ME) bandlimited prediction. The dimensions of the predefined subspaces are in the range from one to five for practical communication systems. The subspace used for ME bandlimited prediction is selected based on a probabilistic bound on the reconstruction error. By contrast, time-variant channel prediction based on non-orthogonal complex exponential basis functions needs Doppler frequency estimates for each propagation path which requires high computational complexity. We compare the performance of this technique under the assumption of perfectly known complex exponentials with that of ME bandlimited prediction augmented with dynamic subspace selection. In particular we analyze the mean square prediction error of the two schemes versus the number of discrete propagation paths