No abstract

A novel quantization-based data-hiding method, called Rational Dither Modulation (RDM), is presented. This method retains most of the simplicity of the conventional dither modulation (DM) scheme, which is largely vulnerable to amplitude scalings and modifies it in such a way that the result becomes invariant to gain attacks. RDM is based on using a gain-invariant adaptive quantization step-size at both embedder and decoder. This causes the watermarked signal to be asymptotically stationary. Mathematical tools are used to determine the stationary probability density function, which is later used to assess the performance of RDM in Gaussian channels. It is also shown that by increasing the memory of the system, it is possible to asymptotically approach the performance of DM, still keeping invariance against gain attacks. RDM is compared with improved spread-spectrum methods, showing that the former can achieve much higher rates for the same bit error probability. Experimental results confirm the validity of the theoretical analyses given in the paper. Finally, a broader class of methods, that extends gain-in- variance to quantization index modulation (QIM) methods, is also presented

Reconfigurable Intelligent Surfaces (RIS) will play a pivotal role in next-generation wireless systems. Despite efforts to minimize pilot overhead associated with channel estimation, the necessity of configuring the RIS multiple times before obtaining reliable Channel State Information (CSI) may significantly diminish their benefits. Therefore, we propose a CSI-free approach that explores the feasibility of optimizing the RIS for the uplink of a communication system in the presence of interfering users without relying on CSI estimation but leveraging solely some a priori statistical knowledge of the channel. In this context, we consider a multiport network model that accounts for several aspects overlooked by traditional RIS models used in Communication Theory, such as mutual coupling among scattering elements and the presence of structural scattering. The proposed approach targets the maximization of the average achievable rate and is shown to achieve performance that, in some cases, can be very close to the case where the RIS is optimized leveraging perfect CSI.

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the environment, such as the distribution of the locations of the users and the distribution of the multipath channels.As a consequence, the proposed approach does not require the estimation of the instantaneous channel state information (CSI) for system optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical CSI still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed.

In this paper, a pervasive monitoring system to be deployed in underground environments is presented. The system has been specifically designed for the so-called “Bottini”, i.e., the medieval aqueducts dug beneath the Centre of Siena, Italy. The results of a measurement campaign carried out in the deployment scenario show that the transmission range of LoRa (Long Range) technology is limited to a maximum of 200 m, thus, making the adoption of a classical star topology impossible. Hence, a Linear Sensor Network topology is proposed based on multi-hop LoRa chain-type communications. In this scenario, an ad-hoc transmission scheme is presented that optimally evaluates the wake-up time of all nodes with the aim of minimizing the average energy dissipation deriving from clock offsets. Numerical results show that the proposed wake-up time optimization leads in the best case to a 50% reduction in power dissipation with respect to a scheme that evaluates the wake-up time in a non-optimal way.