Jpmg Jean-Paul Linnartz

A Coexistence Model of IEEE 802.15.4 and IEEE 802.11b/g

IEEE 802.15.4 was developed to meet the needs for low-rate wireless communication. However, due to its low power, IEEE 802.15.4 is potentially vulnerable to interference by other wireless technologies having much higher power and working in the same industrial, scientific, and medical (ISM) band such as IEEE 802.11 b/g. The paper therefore focuses on the coexistence impact of IEEE 802.11 b/g on the IEEE 802.15.4. In this paper, we present a coexistence model of IEEE 802.15.4 and IEEE 802.11 b/g, which exposes the interactive behavior between these two standards and therefore accurately explains their coexistence performance. The model focuses on two aspects, namely power and timing. These two aspects jointly impose different impacts on the performance of IEEE 802.15.4 networks, depending on coexistence situations. To classify the coexistence situations, we introduce a concept of coexistence range, by extending the concept of sensing and interference ranges across different wireless standards. We characterize the coexistence behavior in each coexistence range and identify for each range the underlying coexistence mechanism and protocol interactions. Analytical models are proposed for the case of saturated traffic and simulation results are presented to validate the model.

Performance comparison for illumination and visible light communication system using buck converters

This paper presents a new analysis of the illumination and communication performance for visible light communication (VLC) with variable pulse position modulation (VPPM). In particular, it addresses the extra power losses due to the data modulation if a traditional buck converter LED driver is adapted for VLC. We compare VLC systems using two previously proposed driver schemes in the aspects of efficiency, illumination power, data rate and BER.

Full MIMO Spatial Filtering Approach for Dynamic Range Reduction in Wideband Cognitive Radios

Wideband cognitive radios (CRs) receive signals from multiple transmitters simultaneously to increase spectrum utilization. Processing a wideband spectrum is challenging due to large dynamic range (DR) of the received signal and required high sampling speed of the ADC. The power consumption of high sampling speed/high-resolution ADCs have been prohibitive for handheld radios. However, in CR applications strong inband signals that pose large DR requirements can be filtered out, since CR needs to detect unused spectrum bands where no signal is present. Spatial domain filtering approaches through use of multiple antennas to reduce DR of the wideband signal are proposed. Algorithms and architectures are developed for vector beamforming (multiple antennas and a single ADC) and full multiple-input multiple-output (MIMO) (multiple antennas with an ADC per antenna) analog spatial filters for adaptive interference suppression. Simulation results indicate that for realistic indoor propagation environments the ADC resolution of an analog beamformer can be reduced by 4 bits when the receiver operates at 2 bits/s/Hz, reducing ADC power consumption by approximately 90%. Moreover, simulations indicate that full MIMO analog spatial filter can reduce ADC resolution with over 3 bits per ADC when the receiver operates at 5 bits/s/Hz, reducing ADC power consumption by approximately 85%.

System study of a 60 GHz wireless-powered monolithic sensor system

State-of-the-art batteryless wireless sensors have separate modules for sensing and energy scavenging. Such separation increases the size and cost of sensors and limits their robustness. To overcome these limitations, we propose a 60 GHz wireless sensor system, which we call the PREMISS system, based on monolithic sensors with on chip sensing, tranceiving, integrated antenna and energy scavenging. In the PREMISS system, a high-power central controller transmits RF energy and information to many low-power low-cost sensors via pencil beams and receive and detect the information from these sensors. In this paper, we present a system study on the PREMISS system highlighting design challenges and practical implementation considerations. From the link budget calculation, we show that in the PREMISS system, a signal to noise ratio of 17 dB at the central controller receiver may be achieved from a sensor 5 meters away. In addition, we also identified that one key challenges in the PREMISS system is the design of good efficiency rectifiers for input power and voltage levels far below those in state-of-the-art 2.4 GHz systems.

Rate adaptation using acknowledgement feedback in finite-state markov channels with collisions

We investigate packet-by-packet rate adaptation so as to maximize the throughput. We consider a finite-state Markov channel (FSMC) with collisions, which models channel fading as well as collisions due to multi-user interference. To limit the amount of feedback data, we only use past packet acknowledgements (ACKs) and past rates as channel state information. The maximum achievable throughput is computationally prohibitive to determine, thus we employ a two-pronged approach. Firstly, we derive new upper bounds on the maximum achievable throughput, which are tighter than previously known ones. Secondly, we propose the particle-filter-based rate adaptation (PRA), which employs a particle filter to estimate the a posteriori channel distribution. The PRA can easily be implemented even when the number of available rates is large. Numerical studies show that the PRA performs within one dB of SNR to the proposed upper bounds for a slowly time-varying channel, even in the presence of multi-user interference.

Front End Power Dissipation Minimization and Optimal Transmission Rate for Wireless Receivers

Most wireless battery-operated devices spend more energy receiving than transmitting. Hence, minimizing the power dissipation in the receiver front end, which, in many cases, is the prominent power consuming part of the receiver, is an important challenge. This paper addresses this challenge by solving two closely related optimization problems. Firstly, we optimize the overall power dissipation in an RF front end consisting of a chain of building blocks to satisfy required overall specifications in gain, linearity and noise figure. We extend this into a second optimization problem, namely to maximize the transmission rate that the receiver can accommodate for a given available receiver battery power budget. In fact, the ratio of this transmission rate vs the available receiver power budget serves as the figure-of-merit that allows a formal optimization where, in particular, the (adjacent channel) interference is a critical factor. Our results include closed-form analytical solutions for certain cases. For high signal power, where the noise is limited by interference, the largest bit/s/Hz per nJ drawn from receiver is reached for a transmission rate of 2.3 bits/s/Hz, irrespective of interference power. Numerical results using practical circuit blocks with 90 nm and 65 nm technologies are in close agreement with the analytical results.

Zero leakage quantization scheme for biometric verification

Biometrics gain increasing interest as a solution for many security issues, but privacy risks exist in case we do not protect the stored templates well. This paper presents a new verification scheme, which protects the secrets of the enrolled users. We will show that zero leakage is achieved if certain criteria are met and we benchmark the performance of this scheme. We quantify performance loss in terms of detection and false acceptance rate and capacity of the biometric channel, which are slightly worse than those of the current leaky methods.

Optimizing throughput for limited receiver circuit power

Maximizing the battery life time of mobile devices and sensor nodes increasingly becomes a challenge, and receiver power consumption tends to become more problematic than delivering adequate transmit power. We address the challenge of achieving the highest possible throughput per Watt of receiver circuit power. Our results show that optimum and adaptive tuning of the front-end parameters of the receiver can result in substantial power savings, compared to the common practice of a design for worst case conditions. We obtain a closed form solution for maximum throughput and the corresponding optimal overall system specifications. We confirm that handling the interference from nearby channels has a large influence, and our analysis concludes that adaptive control of the IP3 performance has an overarching impact. We further describe how the adaptive overall system settings can be translated into optimum gain and IP3 specifications of each of the individual stages that form the receiver cascade, considering both the accumulation of circuit noise and distortion products. The example of a WLAN system is elaborated to illustrate our method.

Effect of buck driver ripple on BER performance in visible light communication using LED

This paper analyses the communication performance for visible light communication (VLC) with Manchester encoded amplitude modulation. In particular, it considers the ripple generated by the LED driver as an important noise contribution for VLC. The ripple depends on the oscillation frequency of the converter which typically is chosen to satisfy other performance criteria such as power conversion efficiency. This paper models the ripple as an additional noise if a buck converter LED driver is adapted for VLC. We argue that ripple can as a worst case be modeled as a random binary offset in the bit detection that randomly affects the distance between the data signal and the decision threshold. We derive expressions for the BER, propose approximations and we compare these with a simulation.

Improved Presence Detection for Occupancy Control in Multisensory Environments

Presence detection is used in occupancy control to dynamically adjust energy-related appliances in smart building applications. Yet, practical applications typically suffer from high sensor unreliability. We propose a computationally efficient approach, based on Hidden Markov Models, to fuse sensor observations from multiple sensors to better estimate user state (presence/absence). Our model considers a realistic scenario, where sensor communication may be limited or unreliable, thus some sensor observations data may be missing for some intervals. Compared to state of art classifiers (Logistic Regression, Naïve Bayes, SVM), our approach achieves improved results while maintaining low computational and memory requirements or even relaxing these. Judging from our experiments, the algorithm appears to work well also in real-world test set-up where user presence and sensors error may not exactly follow our idealized model assumptions.