1,721,046 research outputs found
A survey on game-theoretic approaches to energy-efficient relay-assisted communications
No full textRecent research on game-theoretic approaches for non-cooperative resource allocation in relay-assisted interference channels is reviewed in this paper. The considered utility function is the physical layer energy efficiency, which is measured in bit/J, and denotes the number of bits successfully delivered to the receiver for each energy unit drained from the battery. Both the cases in which the power dissipated in the transmitter circuit is included and not included in the utility functions are considered, and, also, the case in which there is a direct path between transmitters and receivers is briefly addressed. Analytic results on the existence of Nash equilibrium points are given, and extensive numerical results provide insight on the performance of the reviewed resource allocation algorithms
A New Sequential Optimization Procedure and Its Applications to Resource Allocation for Wireless Systems
No full textA novel optimization framework for resource allocation in wireless networks and radar systems is proposed, which merges the methods of maximum block improvement (MBI) and of sequential optimization. A detailed convergence proof is provided, showing that the proposed algorithm is able to monotonically increase the objective value while ensuring that every limit point of the generated variable sequence fulfills the problem first-order optimality conditions under very mild hypothesis. These results extend available convergence results on MBI and sequential optimization, significantly widening the range of applications that can be handled by the proposed framework compared to available approaches. This point is illustrated in detail presenting relevant applications from both the cellular and radar context, which fall under the umbrella of the developed optimization method
Distributed energy-aware resource allocation in multi-antenna multi-carrier interference networks with statistical CSI
No full textResource allocation for energy efficiency optimization in multi-carrier interference networks with multiple receive antennas is tackled. First, a one-hop network is considered, and then, the results are extended to the case of a two-hop network in which amplify-and-forward relaying is employed to enable communication. A distributed algorithm which optimizes a system-wide energy-efficient performance function, and which is guaranteed to converge to a stable equilibrium point, is provided. Unlike most previous works, in the definition of the energy efficiency, not only the users' transmit power but also the circuit power that is required to operate the devices is taken into account. All of the proposed procedures are guaranteed to converge and only require statistical channel state information, thus lending themselves to a distributed implementation. The asymptotic regime of a saturated network in which both the active users and the number of receive antennas deployed in each receiver grow large is also analyzed. Numerical results are provided to confirm the merits of the proposed algorithms
Wireless Inference Gets Smarter: RIS-assisted Channel-Aware MIMO Decision Fusion
No full textWe study channel-aware binary-decision fusion over a shared flat-fading channel with multiple antennas at the Fusion Center (FC). This paper considers the aid of a Reconfigurable Intelligent Surface (RIS) to effectively convey the information of the phenomenon of interest to the FC and foster energy-efficient data analytics supporting the Internet of Things (IoT) paradigm. We present the optimal rule and derive a (sub-optimal) joint fusion rule & RIS design, representing an alternative with reduced complexity and lower system knowledge required. Simulation results for performance are presented showing the benefit of RIS adoption even in a suboptimal case
Deep Learning Power Allocation in Massive MIMO
Full text linkThis work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation profiles for a new set of UEs' positions. The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods. Particularly, the proposed approach does not require the computation of any statistical average, which would be instead necessary by using standard methods, and is able to guarantee near-optimal performance
User Association and Load Balancing for Massive MIMO through Deep Learning
Full text linkThis work investigates the use of deep learning to perform user-cell association for sum-rate maximization in Massive MIMO networks. It is shown how a deep neural network can be trained to approach the optimal association rule with a much more limited computational complexity, thus enabling to update the association rule in real-time, on the basis of the mobility patterns of users. In particular, the proposed neural network design requires as input only the users' geographical positions. Numerical results show that it guarantees the same performance of traditional optimization-oriented methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Mutual information of phase-noise impaired wireless networks
No full textThe mutual information of a MIMO wireless channel, impaired by memoryless phase-noise, is characterized. Both a Line-of-Sight (LOS) MIMO link and a downlink channel with multiple-antenna base station (BS) are studied. For both cases, closed-form approximations of the mutual information in the low-SNR regime are derived. Next, leveraging the obtained approximations, power control policies for achievable rate maximization are designed by means of majorization theory and alternating maximization
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