MC-CDMA Systems: a General Framework for Performance Evaluation with Linear Equalization

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Smart city pilot projects using LoRa and IEEE802.15.4 technologies

Information and Communication Technologies (ICTs), through wireless communications and the Internet of Things (IoT) paradigm, are the enabling keys for transforming traditional cities into smart cities, since they provide the core infrastructure behind public utilities and services. However, to be effective, IoT-based services could require different technologies and network topologies, even when addressing the same urban scenario. In this paper, we highlight this aspect and present two smart city testbeds developed in Italy. The first one concerns a smart infrastructure for public lighting and relies on a heterogeneous network using the IEEE 802.15.4 short-range communication technology, whereas the second one addresses smart-building applications and is based on the LoRa low-rate, long-range communication technology. The smart lighting scenario is discussed providing the technical details and the economic benefits of a large-scale (around 3000 light poles) flexible and modular implementation of a public lighting infrastructure, while the smart-building testbed is investigated, through measurement campaigns and simulations, assessing the coverage and the performance of the LoRa technology in a real urban scenario. Results show that a proper parameter setting is needed to cover large urban areas while maintaining the airtime sufficiently low to keep packet losses at satisfactory levels

Cellular network capacity and coverage enhancement with MDT data and Deep Reinforcement Learning

Recent years witnessed a remarkable increase in the availability of data and computing resources in comm-unication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT -driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks

Partial Equalization for MC–CDMA Systems in Non-Ideally Estimated Correlated Fading

Multicarrier code-division multiple access (MC–CDMA) can support high data rates in next-generation multiuser wireless communication systems. Partial equalization (PE) is a low-complexity technique for combining the signals of subcarriers to improve the achievable performance of MC–CDMA systems in terms of their bit error probability (BEP) and bit error outage (BEO) in comparison with maximal ratio combining, orthogonality restoring combining, and equal-gain combining techniques. We analyze the performance of the multiuser MC–CDMA downlink and derive the optimal PE parameter expression, which minimizes the BEP. Realistic imperfect channel estimation and frequency-domain (FD) block-fading channels are considered. More explicitly, the analytical expression of the optimum PE parameter is derived as a function of the number of subcarriers, number of active users (i.e., the system load), mean signal-to-noise ratio (SNR), and variance of the channel-estimation errors for the aforementioned FD block-fading channel. We show that the choice of the optimal PE technique significantly increases the achievable system load for the given target BEP and BEO

Spatially Distributed Molecular Communications: An Asynchronous Stochastic Model

This letter studies large-scale molecular communication systems by using point processes theory. A swarm of point transmitters randomly placed in a bounded space are considered in conjunction with a single fully absorbing receiver. The transmitters' positions are modeled by a spatial point process, but the global clock assumption, adopted by prior works, is here removed. More precisely, the emission times for each point transmitter are considered as random and are modeled by a non-stationary time-domain point process. We show that, if the intensity function is the same for all time point processes (thus taking the meaning of a distributed input), the average number of received molecules per time unit (receiving rate) can be computed through a convolution: the collective response to a one-molecule emission can be properly interpreted as the impulse response. This models unifies all the widely known transmitter models (exact concentration, Poisson concentration, and timing transmitter), which result as special cases. Analytical expressions for the receiving rate are provided and validated by Monte-Carlo simulations

Inhomogeneous Poisson Sampling of Finite-Energy Signals with Uncertainties in R^d

Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with uncertainty is a crucial problem for a variety of applications. Such a problem generalizes the reconstruction of a deterministic signal and that of a stationary random process in one dimension, which was first addressed by Whittaker, Kotelnikov, and Shannon. In this work we analyze multidimensional random sampling with uncertainties jointly accounting for signal properties (signal spectrum and spatial correlation) and for sampling properties (inhomogeneous sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). The reconstructed signal spectrum and the signal reconstruction accuracy are derived as a function of signal and sampling properties. It is shown that some of these properties expand the signal spectrum while others modify the spectrum without expansion. The signal reconstruction accuracy is first determined in a general case and then specialized for cases of practical interests. The optimal interpolator function is derived and asymptotic results are obtained to show the impact of sampling non-idealities. The analysis is corroborated by verifying that previously known results can be obtained as special cases of the general one and by means of a case study accounting for various settings of signal and sample properties

On the Effect of Combined Equalization for MC-CDMA Systems in Correlated Fading Channels

Equalization techniques are considered in multi carrier-code division multiple access (MC-CDMA) systems to efficiently combine subcarriers contribution and improve the performance. In this paper we analytically investigate a combined equalization technique which consists in performing both pre- equalization at the transmitter and post-equalization at the receiver, by exploiting channel knowledge at both sides. To keep the framework as much general as possible, a parametric partial combining (PC) technique is considered. The analytical framework proposed allows the derivation of the bit error probability in correlated block fading channels and its dependence on the number of subcarriers, the number of active users, the mean signal-to-noise ratio (SNR) averaged over small-scale fading and, above all, the PC parameters, thus allowing the derivation of optimal equalization technique depending on fading levels

Ginibre sampling and signal reconstruction

The spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes. The Ginibre point process (GPP) is a class of determinantal point processes that has been recently proposed for wireless networks with repulsiveness between nodes. A modified GPP can be considered an intermediate class between the PPP (fully random) and the GPP (relatively regular) that can be derived as limiting cases. In this paper we analyze sampling and reconstruction of finite-energy signals in \u211dd when samples are gathered in space according to a determinantal point process whose second order product density function generalizes to \u211dd that of a modified GPP in \u211d2. We derive closed form expressions for sampled signal energy spectral density (ESD) and for signal reconstruction mean square error (MSE). Results known in the literature are shown to be sub-cases of the proposed framework. The proposed analysis is also able to answer to the fundamental question: does the higher regularity of GPP also imply an higher signal reconstruction accuracy, according to the intuition? Theoretical results are illustrated through a simple case study