89 research outputs found
Information credibility modeling in cooperative networks: equilibrium and mechanism design
No full textIn a cooperative network the user equipment (UE) share information with each other for cooperatively achieving a common goal. However, owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is paucity of information on modeling the UEs' decision making behavior, namely as to whether they are willing/able to share genuine information, even though this directly affects the information credibility across the network. Hence, we propose a game theoretic framework for the associated information credibility modelling by taking into account the users’ information sharing strategies and utilities. This framework is investigated under both a homogeneous model and a heterogeneous model. The spontaneous information credibility equilibria of both models are derived and analyzed, including the closed-form analysis of the homogeneous model based on a sophisticated evolutionary game model and on the reinforcement learning based analysis of the heterogeneous model. Moreover, a credit mechanism is designed for encouraging the UEs to share genuine information. Experimental results relying on real-world data traces support our utility function formulation, while our simulation results verify the theoretical analysis and show that all UEs are encouraged by the proposed algorithm to share genuine information with a probability of one, when a credit mechanism is invoked. The proposed modelling techniques may be applied in diverse cooperative networks, including classic wireless networks, vehicular networks, as well as social networks
Downlink channel estimation for massive MIMO systems relying on vector approximate message passing
No full textTo reduce the pilot overhead of downlink channel estimation in massive multiple-input–multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector approximate message passing (VAMP) technique is proposed. More specifically, an a-priori channel model characterized by a multivariate Bernoulli-Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels. Moreover, the hyperparameters of the a-priori model are learned by invoking the expectation maximization (EM) algorithm. Our numerical results demonstrate that the proposed algorithm is capable of reducing the pilot overhead by 50% in massive MIMO systems
Iterative Doppler Frequency Offset Estimation in Low SNR Satellite Communications
No full textSatellite communication systems usually work in low signal-to-noise ratio (SNR) circumstances owning to the limited satellites' link budgets. Large doppler frequency offset in low-SNR satellite communication systems severely influences the performance of frequency synchronization, while the frequency offset correction still remains an open problem under low SNR condition especially for short burst transmission. To solve such a problem in satellite communications, we present a novel method named GP-MASO-MLE, which comprises a coarse correction based on the objective function with Gaussian Process (GP) search and a fine correction based on Maximum Likelihood Estimation (MLE) jointly with turbo iterations. Specifically, the proposed method is appropriate for non-data-aided frequency offset correction in satellite communication systems. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal frequency offset correction within 0.1 dB, moreover, the proposed algorithm has lower computational complexity compared with traditional multi-step search algorithms.</p
Asynchronous multi-class traffic management in wide area networks
No full textThe emergence of new applications brings multi-class traffic with diverse
quality of service (QoS) requirements to wide area networks (WANs), motivating
research in traffic engineering (TE). In recent years, novel centralized and
hierarchical TE schemes have used heuristic or machine learning techniques to
orchestrate resources in closed systems such as datacenter networks. However,
these schemes suffer from long delivery delays and high control overhead when
applied to general WANs. To provide low-delay services, this paper proposes an
asynchronous multi-class traffic management (AMTM) scheme. We first establish
an asynchronous TE paradigm in which distributed nodes locally perform
low-complexity and low-delay traffic control based on link prices, and the TE
server updates link prices to eliminate decision conflicts between edge nodes.
By modeling the asynchronous TE paradigm as a control system with
non-negligible control loop delay, we find that the traditional pricing
strategy cannot simultaneously achieve a low packet loss rate and a low flow
delivery delay. To address this issue, we propose a new pricing strategy based
on the observations of virtual queues in intermediate nodes. We also present a
system design and related algorithms that utilize a dynamic step size mechanism
of link price update. Simulation results show that AMTM can effectively reduce
the end-to-end flow delivery delay
Improving Integrated Terrestrial-Satellite Network Utilization using Near-Optimal Segment Routing
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Divergence minimization approach to joint phase estimation and decoding in satellite transmissions
No full textCarrier Frequency Offset Estimation Using Extended Kalman Filter in Uplink OFDMA Systems
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