Large-scale MIMO systems with a massive number N of individually controlled antennas pose significant challenges for minimum mean square error (MMSE) channel estimation, based on uplink pilots. The major ones arise from the computational complexity, which scales with$N^{3}$, and from the need for accurate knowledge of the channel statistics. This paper aims to address both challenges by introducing reduced-complexity channel estimation methods that achieve the performance of MMSE in terms of estimation accuracy and uplink spectral efficiency while demonstrating improved robustness in practical scenarios where channel statistics must be estimated. This is achieved by exploiting the inherent structure of the spatial correlation matrix induced by the array geometry. Specifically, we use a Kronecker decomposition for uniform planar arrays and a well-suited circulant approximation for uniform linear arrays. By doing so, a significantly lower computational complexity is achieved, scaling as$N\sqrt {N}$and$N \log N$for squared planar arrays and linear arrays, respectively.

Satellite communications (SatCom) have a role of advanced service enablers in the new virtual networks, following 3GPP specifications. Specifically, the satellite peculiar characteristics are of paramount importance to dynamically activate capabilities, such as multicast and broadcast channels, sudden traffic offloading, capacity bonding, and cost-efficient coverage of uncovered areas. A key aspect to achieve a seamless and efficient integration between satellite and terrestrial infrastructures is to make satellite resource management dynamic and with a centralised control of a single orchestrator that has visibility of the end-to-end network. Considering the adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms and the upcoming 5thgeneration of mobile communications (5G), this paper presents the ongoing work within the European Space Agency (ESA) ANChOR project, whose main output will be a data-driven Network Controller and Orchestrator for SatCom networks. This tool will be based on Artificial Intelligence (AI) / Machine Learning (ML)-based techniques to properly allocate resources exploiting some feedback knowledge of the network and it aims to support different services over 5G integrated satellite-terrestrial networks.

Accurate measurement and monitoring of precipitation is crucial for many applications, such as flood and drought risk assessment and management. Conventional meteorological devices for estimating precipitation (i.e., rain gauges, disdrometers, active and passive remote sensors, be they ground-based, spaceborne, or airborne) have their own strengths and weaknesses. The latter are often related to time and space resolution, coverage, and cost. In the last two decades, several studies have been carried out to exploit opportunistic signals of terrestrial microwave communication links to improve precipitation estimation capability. This study describes and evaluates a method to retrieve rainfall rate from the signal-to-noise ratio obtained from commercial interactive digital video broadcasting (DVB) receivers [referred as to smart low-noise blocks (smartLNBs)]. During a 1-year measurement campaign carried out in Tuscany (Italy) with purposely deployed instruments, the precipitation values estimated from a set of SmartLNBs were compared with measurements from co-located rain gauges and disdrometer. The normalized mean absolute error (NMAE) and root-mean-square error (RMSE) obtained comparing the total cumulative precipitation from a SmartLNB and a disdrometer are 48.8% and 7.46 mm, respectively. Encouraging results also come from comparing the total precipitation amounts as measured by the SmartLNBs and by the rain gauges, with values of NMAE (respectively, RMSE) ranging between 44% and 82% (respectively, 5.2 and 11.5 mm).

This paper considers the problem of energy efficiency maximization in the uplink of a cluster of multiple-antenna coordinated access points. A framework for energy efficiency optimization is developed in which the signal-to-interference-plus-noise ratio takes a more general expression than existing alternatives so as to encompass most 5G candidate technologies. Two energy efficiency optimization problems are formulated, also considering quality-of-service (QoS) constraints: 1) network global energy efficiency maximization; 2) worst-case energy-efficient design. These fractional, non-convex problems are tackled by means of fractional programming coupled with sequential convex optimization, and two low-complexity resource allocation algorithms are designed, which are guaranteed to converge to local optima of the non-convex problems. Numerical results show that the proposed algorithm can efficiently balance between the goals of maximizing the energy efficiency and meeting the QoS constraints. Moreover, it is shown that a small sum-rate reduction allows large energy savings.

This work proposes a distributed power allocation scheme for maximizing energy efficiency in the uplink of OFDMA-based HetNets where a macro-tier is augmented with small cell access points. Each user equipment (UE) in the network is modeled as a rational agent that engages in a non-cooperative game and allocates its available transmit power over the set of assigned subcarriers to maximize its individual utility (defined as the user's throughput per Watt of transmit power) subject to a target rate requirement. In this framework, the relevant solution concept is that of Debreu equilibrium, a generalization of the concept of Nash equilibrium. Using techniques from fractional programming, we provide a characterization of equilibrial power allocation profiles. In particular, Debreu equilibria are found to be the fixed points of a water-filling best response operator whose water level is a function of rate constraints and circuit power. Moreover, we also describe a set of sufficient conditions for the existence and uniqueness of Debreu equilibria exploiting the contraction properties of the best response operator. This analysis provides the necessary tools to derive a power allocation scheme that steers the network to equilibrium in an iterative and distributed manner without the need for any centralized processing. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters.

In this paper, we investigate the power control problem in a cooperative network with multiple wireless transmitters, multiple amplify-and-forward relays, and one destination. The relay communication can be either full duplex or half-duplex, and all source nodes interfere with each other at every intermediate relay node, and all active nodes (transmitters and relay nodes) interfere with each other at the base station. A game-theory-based power control algorithm is devised to allocate the powers among all active nodes. The source nodes aim at maximizing their energy efficiency (in bits per Joule per Hertz), whereas the relays aim at maximizing the network sum rate. We show that the proposed game admits multiple pure/mixed-strategy Nash equilibrium points. A Q-learning-based algorithm is then formulated to let the active players converge to the best Nash equilibrium point that combines good performance in terms of both energy efficiency and overall data rate. Numerical results show that the full-duplex scheme outperforms half-duplex configuration, Nash bargaining solution, the max-min fairness, and the max-rate optimization schemes in terms of energy efficiency, and outperforms the half-duplex mode, Nash bargaining system, and the max-min fairness scheme in terms of network sum rate.

In this work, we derive a distributed power control algorithm for energy-efficientuplink transmissions in interference-limited cellular networks, equipped with either multiple or shared relays. The proposed solution is derived by modeling the mobile terminals as utility-driven rational agents that engage in a noncooperative game, under minimum-rate constraints. The theoretical analysis of the game equilibrium is used to compare the performance of the two different cellular architectures. Extensive simulations show that the shared relay concept outperforms the distributed one in terms of energy efficiency in most network configurations.

This work proposes a distributed power allocation scheme for maximizing the energy efficiency in the uplink of non-cooperative small-cell networks based on orthogonal frequency-division multiple-access technology. This is achieved by modeling user terminals as rational agents that engage in a non-cooperative game in which every terminal selects the power loading so as to maximize its own utility (the user's throughput per Watt of transmit power) while satisfying minimum rate constraints. In this framework, we prove the existence of a Debreu equilibrium (also known as generalized Nash equilibrium) and we characterize the structure of the corresponding power allocation profile using techniques drawn from fractional programming. To attain the equilibrium in a distributed fashion, we also propose a method based on an iterative water-filling best response process. Numerical simulations are then used to assess the convergence of the proposed algorithm and the performance of its end-state as a function of the system parameters.

This paper presents an energy-efficient power allocation for relay-aided heterogeneous networks subject to coupling convex constraints, that make the problem at hand a generalized Nash equilibrium problem. The solution to the resource allocation problem is derived using a sequential penalty approach based on the advanced theory of quasi variational inequality, which allows the network to converge to its generalized Nash equilibrium in a distributed manner. The main feature of the proposed approach is its decomposability, which leads to a two-layer distributed algorithm with provable convergence.

In this work, a distributed and iterative algorithm for uplink power control in the initial contention-based synchronization procedure of orthogonal frequency-division multiple access networks is derived. This is achieved by letting the mobile terminals maximize their own energy efficiency in terms of power consumption and average synchronization time exploiting a quantized feedback from the base station. The problem is formulated as a constrained finite noncooperative game in which the transmit powers are chosen from a discrete set. The theoretical solution is investigated and compared to the case of continuous powers. In addition, comparisons with existing alternatives (with or without perfect feedback) are made in terms of power expenditure, average synchronization time, and estimation accuracy.

This paper introduces a game-theoretic formulation for the problem of resource allocation in an infrastructure CDMA wireless communication network, focusing on the issue of initial code synchronization. To capture the tradeoff between obtaining good synchronization performance and saving as much energy as possible, we propose a noncooperative game in which each transmitter-receiver pair seeks to maximize the ratio of the probability of code alignment detection to the transmitted energy per acquisition, under a constraint on the maximum probability of spurious code locks - this is achieved by properly setting the receiver threshold and the transmit power. Our framework also considers the coexistence between different types of synchronizers and different QoS requirements. The generalized Nash solution of the game is investigated and closed-form expressions are derived and compared with simulation results for an iterative decentralized algorithm.

This paper proposes a game-theoretic approach to the problem of resource allocation for the uplink of a CDMA wireless communication network, in which terminals code-locked to the access point (in-sync users) coexist with some terminals still in the code acquisition stage (out-of-sync users). To capture the tradeoff between obtaining good performance and saving as much energy as possible for both classes, we propose a noncooperative game with strict incomplete information based on the energy-efficient criterion. This formulation allows us to devise an iterative algorithm that enables each terminal to locally regulate its optimal transmit power while ignoring all others' synchronization phases. A numerical example is reported to show the improvements provided by the proposed approach.

This paper investigates the performance of distributed power control techniques in the uplink of a flat-fading CDMA wireless network, based on noncooperative game theory. Two energy-efficient criteria, tailored for data detection and code synchronization, are compared in terms of achieved utility at the Nash equilibrium, showing that the optimal power allocation devised for data detection is not the best solution during synchronization, and analogously the optimal allocation for code acquisition does not provide the best performance during data detection. Closed-form expressions for the optimal powers at the Nash equilibrium are derived, and a unified formulation is proposed to allow a more elaborated power allocation method to be adopted, in which the terminals, using the synchronization-oriented approach, can revert to the data-detection optimal criterion after synchronization is over.

This paper proposes a game-theoretic approach to the problem of resource allocation for a CDMA wireless communication network, focusing on code synchronization. To capture the tradeoff between obtaining good synchronization performance and saving as much energy as possible, we propose a noncooperative game in which each pair transmitter-receiver in the network seeks to maximize the ratio of the probability of detection to the transmitted energy per bit by setting its transmit power and its optimum detection threshold, under a constraint on the maximum probability of spurious code locks. This framework also considers the coexistence between different types of synchronizers in the network and different QoS requirements. The Nash solution of the game is investigated and closed-form expressions are derived and compared with simulation results for a decentralized resource control algorithm.

This paper studies the performance of partial-Rake (PRake) receivers in impulse-radio ultrawideband wireless networks when an energy-efficient power control scheme is adopted. Due to the large bandwidth of the system, the multipath channel is assumed to be frequency-selective. By making use of noncooperative game-theoretic models and large-system analysis tools, explicit expressions are derived in terms of network parameters to measure the effects of self-interference and multiple-access interference at a receiving access point. Performance of the PRake receivers is thus compared in terms of achieved utilities and loss to that of the all-Rake receiver. Simulation results are provided to validate the analysis.