This paper investigates the performance limit of position estimation in a three dimensional (3D) molecular diffusion environment. Specifically, we consider a realistic molecular transmission process based on chemical-physical laws and a Poisson distribution of the received molecules. We derive a closed form expression of the Cramér-Rao Bound as a function of the system parameters, such as the number of molecules, the estimation time, the distance of the target, the constant reaction rate and the reagents concentration showing how they impact the localization estimation.

Systems that combine sensing and communication functionalities are gaining interest for several possible applications related to the internet of things (IoT) and the upcoming 6G mobile radio networks. Studies have recently been proposed to find the optimal tradeoff between sensing and communication performance. However, these studies assume continuous transmission and thus consider that the time available for estimation can always be large enough to achieve some desired accuracy. Moreover, in line with this assumption, communication performance is measured via the well-known Shannon capacity, which implicitly assumes indefinitely long error correction codes. However, in the case of short packet transmissions, such as the case in several IoT applications, the above assumption is unrealistic, as the length of the transmitted packet limits that of the error correction code and the observation time for parameter estimation. Therefore, this paper aims at investigating the optimal tradeoff between the data transmission and the target localization capabilities in a finite block-length regime, by making use of some typical metrics of the finite length information theory. In particular, the optimal beamforming, which minimizes the Cramér Rao bound of target localization, is derived under a constraint over the block error probability for given packet length values.

This paper analyzes distributed molecular communications with multiple transmitters and multiple receivers randomly placed in a 3D space. Receivers, whose positions are modeled by a homogeneous Poisson point process, are assumed to be passive and spherical. Since they are not dimensionless, their presence affects the statistics of transmitter points, resulting in a Poisson hole process, instead of the Poisson process usually adopted in previous works on multiple transmitters. Moreover, molecules emissions from different transmitters are considered asynchronous, according to a recently proposed stochastic model. Here, the intensity function of the time point processes modeling molecules emissions is given a further meaning; in particular, it is considered as the propensity function of a chemical reaction. The average amount of received molecules per time unit (receiving rate) is evaluated accordingly for each receiver. When the swarm of receivers is considered as an equivalent, single, spatially distributed receiver, the average amount of received molecules per time and volume units (receiving rate density) is introduced and analytically evaluated.

Spatial field reconstruction is important for a variety of applications based on environmental monitoring via Internet-of-Things and vehicular communications. This work develops a framework for the analysis of multidimensional stochastic sampling in vehicular crowd sensing, where samples are gathered from sensors on vehicles. Vehicular crowd sensing performance in terms of reconstruction mean-square error is compared to that obtainable with fixed installation. In the first case, sensors are assumed to be randomly distributed over the monitored area, while, in the latter, they are considered as regularly placed in a lattice. In addition, the positions of the mobile nodes are assumed non perfectly known at the interpolator. By extending recent results on multidimensional stochastic sampling, it is shown that a high field reconstruction accuracy can be obtained by vehicular crowd sensing even in cases where fixed infrastructure would lead to insufficient sampling.

The deployment of sensing nodes is crucial for applications relying on the reconstruction of spatial fields. Theoretical analysis usually assumes that nodes are distributed according to a homogeneous Poisson point process (PPP), in which nodes positions are stochastically independent. However, realistic scenarios for crowd sourcing and Internet of Things call for clustered layouts of sensing nodes, for which homogeneous PPP is not appropriate. This paper analyzes sampling and reconstruction of finite-energy signals in Rd, with samples gathered in space according to a Gauss-Poisson point process (G-PPP), which has been recently proposed to model node spatial distribution with attraction (as in clustering). In particular, it is shown that attraction between nodes modeled by G-PPP reduces the reconstruction accuracy with respect the case of homogeneous PPP with the same intensity. This represents the opposite case of the repulsion effect, which was investigated in a previous work relying on Ginibre point process sampling.

This paper investigates the design of non-binary protograph low-density parity-check codes for the Poisson channel with m-ary pulse position modulation. The field order over which the code is constructed is matched to the pulse position modulation order yielding a coded modulation scheme. The optimization of the low-density parity-check code structure is performed via protograph density evolution on a surrogate m-ary erasure channel. The surrogate design is illustrated to be not only accurate, but also robust for a range of practical values of channel background noise and various modulation orders. As a result the proposed codes show excellent performance over the Poisson channel with pulse position modulation outperforming competing schemes. As a side-product of this paper, finite-length benchmarks on the block error probability are provided, together with a union bound to characterize the code performance in the error floor region.

Resource allocation is a challenging issue in multiuser wireless systems. Since users are not all in the same conditions and do not achieve the same performance given the same amount of resources, resource allocation must typically deal with the following two conflicting objectives: on the one hand, the overall performance should be maximized, tending to prioritize few users in better conditions, and on the other hand, fairness among users should be maximized, consequently reducing the overall performance. Identifying the compromise that is somehow optimum is all but simple and this aspect is normally worsened by the absence of a curve describing how the maximum performance varies changing the accepted level of fairness. To cover this gap, in this paper, we propose a unified and general mathematical formulation of the optimal performance versus fairness tradeoff in multiuser wireless communication systems. Differently from the existing literature, our approach is based on the generalized Lagrange method and adopts general definitions for both performance and fairness. Besides the exact solving equations describing how the maximum performance varies with fairness, we also derive a simpler lower bound with reduced computational cost. Example results are provided for two case studies, respectively, concerning linear and logarithmic dependence of performance on resources.

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 ℝdwhen samples are gathered in space according to a determinantal point process whose second order product density function generalizes to ℝdthat of a modified GPP in ℝ2. 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.

Vehicular ad hoc networks (VANETs) drive new challenging research issues at different layers of the protocol pillar, from physical to application. Focusing on the medium access layer, one of the most promising and not yet fully explored option is the adoption of orthogonal frequency division multiple access (OFDMA). Besides guaranteeing high spectral efficiency and effective resource allocation, it relies at the physical layer on orthogonal frequency division multiplexing, which allows repetitions of the same signals to be opportunistically combined at the receiver even if they are not perfectly synchronized. Exploiting this property, here we suggest and discuss the use of OFDMA for alert message flooding in VANETs to increase reliability and resource usage efficiency. The achievable benefit and the potential drawback (in terms of high delivery delay) are here assessed through a simple yet realistic model that allows to compare, in highway scenarios, OFDMA with carrier sensing multiple access/collision avoidance, currently adopted by IEEE 802.11p for distributed communications in high mobility scenarios.

Optimal resource allocation is an outstanding issue in wireless communication systems. In this work we focus on the ever challenging optimization of the tradeoff between throughput and fairness. Specifically, we propose a novel framework to analytically derive the maximum average throughput versus fairness under the assumptions that the throughput of each user i) increases if more resources are allocated to him and ii) depends on how many resources and not which resources are allocated. We achieve a general formulation of the tradeoff optimization problem and we also derive and validate a closed form solution in those scenarios where throughput linearly depends on resources, which cover several realistic cases. Besides these valuable results, the framework also lays solid basis toward a more general solution.

Wireless sensor networks (WSNs) have a large number of applications, number of applications. Among them, the estimation of spatial processes from sparse sensing nodes is important for environmental monitoring. In this work we analyze the process estimation accuracy for a randomly deployed self-organizing WSN taking into account crucial aspects such as random sampling, nodes' connectivity, communications protocols, interpolation techniques, and uncertainty of nodes' positions. These aspects are typically not considered into a common framework. The joint analysis of all these aspects enables a WSN designer to understand the role of interpolation techniques and nodes' position uncertainty on process estimation to reach a given target estimation accuracy.

The architecture of a digital On-Channel Repeater (OCR) for DVB-T/H signals is described in this paper. The presence of a coupling channel between the transmitting and the receiving antennas gives origin to one or more echoes, having detrimental effects on the quality of the repeated signal and critically affecting the overall system stability. A low-complexity echo canceller unit is then proposed, performing a coupling channel estimation based on the local transmission of low-power training signals. In particular, in this paper we focus on the stability issues which arise due to the non perfect echo cancellation. An upper bound on the probability of instability of the system is analytically found, providing useful guidelines for conservative OCR design, and some performance figures concerning different propagation scenarios are provided.

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.

In this paper we analytically evaluate the downlink performance of a multi-carrier code division multiple access (MC-CDMA) system by employing partial combining when multiple antennas at the transmitter or at the receiver are considered. It is known that for single reception/transmission, the partial combining technique depends on a parameter which can be optimized as a function of the number of subcarrier, the number of active users and the SNR improving the performance with respect to other classical techniques (such as maximal ratio combining, equal gain combining, or orthogonality restoring combining). In this paper we extend the analysis considering also multiple antennas aiming at showing how the spatial diversity affects the performance as a function of the partial combining parameter.

This paper addresses the performance evaluation of multi-carrier code division multiple access systems. A partial equalization technique depending on a parameter adapted to different conditions such as the number of sub-carriers, the number of active users and the mean signal-to-noise ratio is evaluated for the down-link. We analytically derive the performance of this system and the value of the equalization parameter that maximizes the performance. The partial equalization technique is shown to have the same complexity of the well known maximal ratio combining, equal gain combining and orthogonality restoring combining techniques, with significative performance improvement. Analytical results are compared with simulations showing a perfect agreement