The distributed nature of cellular networks is one of the main enablers for integrated sensing and communication (ISAC). For target positioning and tracking, making use of bistatic measurements is non-trivial due to their non-linear relationship with Cartesian coordinates. Most of the literature proposes geometric-based methods to determine the target's location by solving a well-defined set of equations stemming from the available measurements. The error covariance to be used for Bayesian tracking is then derived from local Taylor expansions. In our work we adaptively fuse any subset of bistatic measurements using a maximum likelihood (ML) framework, allowing to incorporate every possible combination of available measurements, i.e., transmitter angle, receiver angle and bistatic range. Moreover, our ML approach is intrinsically flexible, as it can be extended to fuse an arbitrary number of measurements by multistatic setups. Finally, we propose both a fixed and dynamic way to compute the covariance matrix for the position error to be fed to Bayesian tracking techniques, like a Kalman filter. Numerical evaluations with realistic cellular communications parameters at mmWave frequencies show that our proposal outperforms the considered baselines, achieving a location and velocity root mean square error of 0.25m and 0.83m/s, respectively.

The surge of massive antenna arrays in wireless networks calls for the adoption of analog/hybrid array solutions, where multiple antenna elements are driven by a common radio front end to form a beam along a specific angle in order to maximize the beamforming gain. Many heuristics have been proposed to sample the angular domain by trading off between sampling step size and overhead, where arbitrarily small angular step size is only attainable with infinite sampling overhead. We show that, for uniform linear and rectangular arrays, lossless reconstruction of the array’s angular responses at arbitrary angular precision is possible using a finite number of samples without resorting to assumptions of angular sparsity. The proposed method, (SARA), defines how many and which angles to be sampled and the corresponding reconstruction. This general solution to scan the angular domain can therefore be applied not only to beam acquisition and channel estimation, but also to radio imaging techniques, making it a candidate for future integrated sensing and communications (ISAC). Extensive simulation results for target detection and radio imaging have demonstrated clear advantages of SARA over other considered baselines, both in terms of angular reconstruction performance and computational complexity.

In the framework of 5G-and-beyond Industry 4.0, jamming attacks for denial of service are a rising threat which can severely compromise the system performance. Therefore, in this paper we deal with the problem of jamming detection and mitigation in indoor factory deployments. We design two jamming detectors based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing and consider jamming mitigation with frequency hopping and random scheduling of the user equipments. We then evaluate the performance of the system in terms of achievable block error rate (BLER) with ultra-reliable low-latency communications traffic and jamming missed detection probability. Simulations are performed considering a 3rd Generation Partnership Project spatial channel model for the factory floor with a jammer stationed outside the plant trying to disrupt the communication inside the factory. Numerical results show that jamming resiliency increases when using a distributed access point deployment and exploiting channel correlation among antennas for jamming detection, while frequency hopping is helpful in jamming mitigation only for strict BLER requirements.

Future cellular networks are intended to have the ability to sense the environment by utilizing reflections of transmitted signals. Multi-dimensional sensing brings along the crucial advantage of being able to resort to multiple domains to resolve targets, enhancing detection capabilities compared to one-dimensional (1D) estimation. However, estimating parameters jointly in 5G New Radio systems poses the challenge of limiting the computational complexity while preserving a high resolution. To that end, we make use of channel state information (CSI) decimation for MUltiple SIgnal Classification (MUSIC)-based joint range-angle of arrival estimation. We further introduce multi-peak search routines to achieve additional detection capability improvements. Simulation results with orthogonal frequency-division multiplexing (OFDM) signals show that we attain higher detection probabilities for closely spaced targets than with 1D range-only estimation. Moreover, we demonstrate that for our considered 5G setup, we are able to significantly reduce the required number of computations due to CSI decimation.

Network slicingmight radically change the relations among different actors of the telecommunications ecosystem, where new players, active in different markets, could benefit of tailored connectivity services based on different business strategies. We argue that for fully exploiting the opportunities offered by network slicing, dynamic sharing of resources is crucial not only for efficiency and cost savings, but also for enabling a resource negotiation that can unleash the potential of new business relations. We develop an automated mechanism that allows tenants to take strategic decisions to optimize the management of their slices based on their instantaneous demands and model their interaction as in marketplace. We integrate our solution, based on game theory, on a 3GPP calibrated system level simulator, where a slice-aware scheduler enforces the tenants’ decisions at the Nash Equilibrium (NE). We compare our proposal with a static baseline, that assigns a fixed share of resources to each slice, and show that, by dynamically trading resources in the market, tenants achieve lower costs, and, therefore, higher profits. We provide an algorithmic implementation that guarantees the convergence to a single NE and test the computational complexity of our algorithm to an increasing number of slices in the system.

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complexity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3rd generation partnership project (3GPP) calibrated channels, showing the validity of our proposal.

The key challenge in admission control in wireless networks is to strike an optimal trade-off between the blocking probability for new requests while minimizing the dropping probability of ongoing requests. We consider two approaches for solving the admission control problem: i) the typically adopted threshold policy and ii) our proposed policy relying on reinforcement learning with neural networks. Extensive simulation experiments are conducted to analyze the performance of both policies. The results show that the reinforcement learning policy outperforms the threshold-based policies in the scenario with heterogeneous time-varying arrival rates and multiple user equipment types, proving its applicability in realistic wireless network scenarios.

This work considers an Industry 4.0 scenario, where many heterogeneous sensors must communicate their data to a central factory management function. Unlike the general trend in the current literature, where wireless sensor networks (WSNs) are assumed to have homogeneous quality of service (QoS), we address the more challenging scenario where each sensor node has a different set of reliability and latency requirements to fulfill. Accordingly, a QoS-aware wireless sensor network (QAW) routing protocol is proposed to satisfy the different QoS requirements by each sensor node, while maximizing the network lifetime, thanks to a novel WSN hierarchical structure. The proposed QAW protocol accounts for the total energy of the nodes and their respective QoS requirements when establishing the routing structure. Periodical updates are proposed to guarantee an optimized network lifetime. Simulation experiments show that the proposed QAW protocol is able to enforce the desired QoS significantly better than the state-of-the-art approaches, while it rivals them in terms of energy consumption and preserves network lifetime. The presented results further confirm benefit of multi-hop WSN structure in improving network lifetime, when compared with single-hop sensor networks that minimize latency even when it is not strictly necessary.

Network slicing provides a key functionality in emerging 5G networks, and offers flexibility in creating customized virtual networks and supporting different services on a common physical infrastructure. This capability critically relies on a MAC scheduler to deliver performance targets in terms of aggregate rates or resource shares for the various slices. A crucial challenge is to enforce such guarantees and performance isolation while allowing flexible sharing to avoid resource fragmentation and fully harness channel variations. In the present paper we propose a MAC scheduler which meets these objectives and preserves the basic structure of utility-based schedulers such as the Proportional Fair algorithm in terms of per-user scheduling metrics. Specifically, the proposed scheme involves counters tracking the aggregate rate or resource allocations for the various slices against pre-specified targets, and computes offsets to the scheduling metrics accordingly. This design provides transparency with respect to other scheduling modules, such as link adaptation and beam-forming. We analytically establish that the proposed scheme achieves optimal overall throughput utility subject to the various slicing constraints. In addition, extensive 3GPP-compliant simulation experiments are conducted to assess the impact on best-effort applications and demonstrate substantial gains in overall throughput utility over baseline approaches.

Vehicular networking belongs to one of the most attractive techniques under the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communication system. It opens diverse new concepts and applications, e.g. Vehicular-to-Vehicular (V2V), Vehicular-to-Infrastructure (V2I). One of the key components to support autonomous driving is to allow vehicles to share information about their awareness of the environment, i.e. perception layer of neighbor vehicles. In this paper, we focus on low overhead, low latency MAC layer design, enabling perception layer sharing in V2V environment, and PHY layer design, enabling Multi-User Detection (MUD) with V2V environment by means of Non-Orthogonal Multiple Access (NOMA), which is one of the key physical layer technologies of 5G New Radio (NR). Numerical results of link level simulation exhibit that our solution generates and exchanges the NOMA user signature flexibly, i.e. supports both synchronous and asynchronous traffic with low bit error rate. Furthermore, it enables small packet communication with low computational complexity, thus low latency.

One of the disruptive innovations introduced by 5G networks is the opportunity for a new group of stakeholders to be actively involved in the management of network slices with the role of tenants. This allows to go beyond the user-centric QoS paradigm of 4G, and to include tools for handling the aggregate performance of multiple services and user groups and to focus on slice resource management, also at the new 5G NR interface. So far, research efforts have privileged a first solution based on the concept of isolation between slices. However, proposed solutions are not particularly efficient due to the loss of pooling gains, and not very reliable due to variable channel conditions that with slice limited resources make performance not easily predictable. We propose a slice management framework where the shared resources are negotiated by tenants in a real-time market based on slice instantaneous demands. Our model, based on game theory, allows tenants to optimize their service strategies acquiring resources when and where it is necessary, according to the level of quality and reliability requested by the specific traffic types they handle. In this paper, we focus on modeling the game theoretical framework and on characterizing its equilibria in a multi-tenant scenario.

Accurate positioning of vehicles and pedestrians is crucial for enhancing road safety. In this paper, we propose and compare two implementations based on Unscented Kalman Filter (UKF) and Particle Filter (PF) to perform trajectory estimation with sensor fusion. For the latter, a novel soft map-matching technique is applied on top of a PF. The main benefit of our method is the possibility of detecting reliably critical situations, like vehicles skidding off the road. Moreover, we can reduce the positioning error by 45% w.r.t. prior art approaches. Our solution can be implemented as a cloud service in the 5G mobile radio network.

One of the key challenges in next generation 5G networks is to deliver Ultra-Reliable Low-Latency Communications (URLLC). Recent advances in information theory about principles that govern short packet transmissions pointed out that, for the URLLC typical short packet dimension, achieving higher reliabilities comes at the price of a lower maximum achievable rate, thus reducing spectral efficiency. Hence, retransmissions are used in LTE and planned for 5G, in order to achieve reliability with a better resource consumption, at the price of increased packet latency. Keeping in mind the stringent requirements for URLLC, in this paper we analyze the tradeoffs and limitations of retransmission strategies considered in the literature, either too demanding in terms of wireless resources and aggressive URLLC performance or the contrary. Then we propose a novel scheme, whose purpose is to match the URLLC requirements, minimizing the resource consumption. We evaluate the schemes through simulations and highlight the advantages of the proposed scheme, providing also insights on the performance HARQ techniques in URLLC scenarios.

Our scientific community faces a sort of paradox. A large bulk of work has been done on data-oriented techniques devised to improve peer reputation and knowledge extraction from data, so as to improve trustworthiness of digital services involving coordination and cooperation among heterogeneous peers. But, perhaps surprisingly, to the best of our knowledge, such techniques have rarely been applied to the (for our own community, crucial) process of reducing noise in the process of peer reviewing our own papers. Goal of this work is to provide initial insights on the applicability of methodologies and tools from inferential statistical to the field of peer review quality control. Our contribution is threefold. First, we propose a statistical model where each technical program committee member (reviewer) is characterized as random noise added to the “actual” value of the paper. Second, we provide an iterative data-oriented approach based on Expectation-Maximization devised to estimate mean value and variance of the noise added by each reviewer; our approach uses only the ratings provided by the reviewers themselves and does not rely on any additional source of a-priori knowledge. Third, we make use of the estimated mean values and variances to improve the accuracy of paper's evaluation and ranking.

In this paper spatial loading in a reciprocal time-varying multiple-input multiple-output (MIMO) channel is considered. We take inspiration from a blind iterative algorithm proposed in the literature to estimate the singular vectors associated to the dominant singular value to perform beamforming. Starting from that, we introduce an iterative algorithm that can estimate all the singular vectors and the associated singular values of the channel matrix. Then the estimated singular vectors are used to transmit over the parallel sub-channels and the associated singular values are considered to implement the rate and power loading algorithm described in this paper. A procedure based on the joint use of the two considered algorithms can be adopted to adaptively maximize the total rate for a given error rate performance and a given constraint on the average transmitted power. Simulation results are used to demonstrate the effectiveness of our approach compared to blind iterative beamforming transmission.