Andre Fonseca dos Santos

Looking beyond green cellular networks

This paper introduces a new network architecture for green cellular networks. The basic concept is to separate signaling and data in the wireless access network. Transmitting the signaling information separately maintains coverage even when the whole data network is adapted to the current load situation. Such network-wide adaptation can power-down base stations when no data transmission is needed and, thus, promises a tremendous increase in energy efficiency. On top of that, the signaling network keeps users connected while consuming only a fraction of the overall energy

M2M massive wireless access: Challenges, research issues, and ways forward

In order to make the Internet of Things a reality, ubiquitous coverage and low-complexity connectivity are required. Cellular networks are hence the most straightforward and realistic solution to enable a massive deployment of always connected Machines around the globe. Nevertheless, a paradigm shift in the conception and design of future cellular networks is called for. Massive access attempts, low-complexity and cheap machines, sporadic transmission and correlated signals are among the main properties of this new reality, whose main consequence is the disruption of the development of the current cellular standards. Here, we provide insights and introduce potential solutions for the cellular radio protocol that will allow the efficient support of Machine-to-Machine (M2M) communications. The paper focuses on the massive aspect of M2M. We will introduce PHY and MAC approaches such as Coded Access Reservation, Coded Random Access and the exploitation of multiuser detection in random access. Additionally, we will show how the properties of machine originated signals, such as sparsity and spatial/time correlation can be exploited. The end goal of this paper is to provide motivation and research guidelines for enabling future networks to support efficiently M2M communications.

Energy consumption analysis of wireless networks using stochastic deployment models

This paper aims to discuss the influence of adjustable base station (BS) power parameters, such as power consumed during active mode and sleep modes, on the overall energy consumption of a network and highlight potential energy savings that can be achieved by the introduction of sleep modes. A BS density that can satisfy the demands of a given user density - defined by a daily traffic profile - can be found using the relationship between spatially averaged rate, user density, and BS density established here. The underlying framework for this relationship assumes users and BSs to be independently marked point processes in ℝ2. A power model that is an affine function of the BS density is used to determine the overall energy consumption of the network at full load, and these values are compared to those that incorporate sleep modes to utilize the minimum number of BSs that satiate the demands of a user density that varies during the course of the day. The relationship established between spatially averaged rate, user density, and BS density forms the main result of this paper, based on which it can be inferred that the introduction of sleep modes results in substantial energy savings when the load is seldom full.

Application of protograph-based LDPC codes for UWB short range communication

We studied the behavior of an iterative receiver using protograph based LDPC codes (PG-LDPCC) in an UWB short range communication system. An EXIT-Chart-Analysis is applied by means of density evolution of protographs. We show that AWGN-optimized protograph ensembles perform also well on frequency-flat UWB channels, outperform rate-compatible punctured convolutional codes (RCPC), but degrade with increasing intersymbol interference (ISI). Slight modifications of the underlying protograph can improve this performance, but lead to a trade-off between the minimum required SNR and the minimum achievable BER. It is shown that both thresholds can be predicted by the applied tools.

On the Impact of Sleep Modes and BW Variation on the Energy Consumption of Radio Access Networks

This paper tries to analyze the energy saving capabilities of two common power saving techniques being suggested - introduction of sleep modes and bandwidth (BW) variation. The framework for this analysis assumes users and base stations (BSs) to be independently marked point processes in R2. The relationship between spatially averaged rate, user density, and base station density, which is an extension of findings in [1], is used in an affine power model to estimate the energy that can be saved by the two methods under consideration. The primary contribution of this paper constitutes an analytic relationship between spatially averaged rate, user density, BS density, transmit power, and the noise power. Another key contribution is a proof that shows that power saved by using sleep modes (or turning off BSs) is always greater than the power saved by varying the BW, for a system model implementing an affine power model (described here) when traffic densities below full load are considered.

A random access and multiuser detection approach for massive access of low-complexity machine communications in cellular networks

Machine-to-Machine (M2M) communication has recently attracted significant interest, and is expected to play a major role in future wireless communication. Various M2M applications are being considered for integration to modern cellular networks including tracking, metering and eHealth, thus benefiting from the wide coverage and lower deployment costs. However, there exist applications supported by low end machine and sensor terminals constrained by limited battery, transmit power, complexity and cost, which suffer from the bulky signal processing and control overheads of existing systems making way for investigating new MAC and PHY approaches. In this paper we present a simple and efficient cross layer design for operation at low transmit powers and bandwidth, accommodating applications with sporadic, low volume transmissions and relatively relaxed QoS requirements. A wide range of monitoring and sensing applications fall in this category and will be a corner stone for realizing future Smart Cities. Using simulations, we identify the bounds on maximum supported machine terminals in a cell with above application characteristics, and also investigate the transmission latency and energy efficiency of the machine terminals.

Iterative Equalization and Decoding for Short Range Communications at 60 GHz

Progress in device technology lets coherent communication schemes for short range communications at 60 GHz appear as a feasible approach in the near future. However, impairments due to nonlinearities that limit the achievable SNR values as well as strongly variable transmission conditions with their associated SINR variations should be taken into account. In this work we propose to use LDPC codes that can be matched to channel conditions and equalizer performance by pilot and data block length as well as code rate adaptation for this task. We demonstrate that we can outperform rate-compatible punctured convolutional RCPC codes in terms of required SNR for semiblind operation with iterative processing at the receiver with LDPC codes based on Accumulate-Repeat-Accumulate (ARA) codes. Such codes appear suitable for high data rates, because they allow efficient encoder and decoder implementations. Index Terms – Turbo Equalization, semiblind channel estimation, short range communications, LDPC codes

Combining super exponential algorithm with logic strings and second order statistics for blind channel estimation

We combine the characteristics of the Super Exponential (SE) method with Logic Strings and Second Order Statistics (SOS) to blindly estimate a multipath channel. The estimator benefits from the good quality of estimation inherent of SOS methods to estimate the magnitude of a channels transfer function allied to the fact that the SE can estimate the channel phase response, and that Logic String estimator can resolve the remaining phase ambiguity inherent to SE and SOS estimators. It results in an estimator without any phase ambiguity with a better performance than the traditional SE method and without severe channel code restrictions as pure Logic String estimators.