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This paper studies the problem of developing an efficient signal sampling framework for distributed blind spectrum sensing, together with the corresponding local cognitive radio (CR) decision and fusion rules. A novel collaborative sampling and binary output (CSBO) generating scheme is proposed to overcome the disadvantage of traditional energy detectors for blind spectrum sensing, in which the signal-to-noise ratio (SNR) and level of noise power at the CRs are unknown and thus the decision threshold of local energy detectors cannot be specified. In designing the fusion rule in conjunction with the proposed CSBO scheme, both fixed-sample-size tests and sequential tests are considered. It is theoretically shown that the proposed CSBO scheme outperforms traditional blind cooperative energy detectors with uniform sampling schemes in terms of its ability to increase the amount of information acquired under a given number of samples. Simulation results confirm the theoretical analyses, and demonstrate that, compared with the fixed-sample-size test, the sequential test requires fewer observations on average to achieve comparable detection performance. The reduction in the expected sample size needed in the sequential CSBO test when compared to the fixed-sample-size CSBO can reach 50% when primary signal is present. [ABSTRACT FROM AUTHOR]

This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations. [ABSTRACT FROM AUTHOR]

A circulant-permutation-based spatially-coupled (SC) code is constructed by partitioning the circulant permutation matrices (CPMs) in the parity-check matrix of a block code into several components and piecing copies of these components in a diagonal structure. By connecting several SC codes, multi-dimensional SC (MD-SC) codes are constructed. In this paper, we present a systematic framework for constructing MD-SC codes with notably better cycle properties than their one-dimensional counterparts. In our framework, the multi-dimensional coupling is performed via an informed relocation of problematic CPMs. This work is general in the terms of the number of constituent SC codes that are connected together, the number of neighboring SC codes that each constituent SC code is connected to, and the length of the cycles whose populations we aim to reduce. Finally, we present a decoding algorithm that utilizes the structures of the MD-SC code to achieve lower decoding latency. Compared to the conventional SC codes, our MD-SC codes have a notably lower population of small cycles, and a dramatic BER improvement. The results of this work can be particularly beneficial in data storage systems, e.g., 2D magnetic recording and 3D Flash systems, as high-performance MD-SC codes are robust against various channel impairments and non-uniformity. [ABSTRACT FROM AUTHOR]

In sponsored content and service markets, the content and service providers are able to subsidize their target mobile users through directly paying the mobile network operator to lower the price of the data/service access charged by the network operator to the mobile users. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the content/service providers. In this paper, we study the interactions among the three parties in the market, namely, the mobile users, the content/service providers, and the network operator, as a two-level game with multiple Stackelberg (i.e., leader) players. Our study is featured by the consideration of global network effects owning to consumers’ grouping. Since the mobile users may have bounded rationality, we model the service-selection process among them as an evolutionary-population follower sub-game. Meanwhile, we model the pricing-then-sponsoring process between the content/service providers and the network operator as a non-cooperative equilibrium searching problem. By investigating the structure of the proposed game, we reveal a few important properties regarding the equilibrium existence and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence of the proposed algorithm and demonstrate how sponsoring helps improve both the providers’ profits and the users’ experience. [ABSTRACT FROM AUTHOR]

Radio frequency identification technology has been widely used in missing tag detection to reduce and avoid inventory shrinkage. In this application, promptly finding out the missing event is of paramount importance. However, the existing missing tag detection protocols cannot efficiently handle the presence of a large number of unexpected tags whose IDs are not known to the reader, which shackles the time efficiency. To deal with the problem of detecting missing tags in the presence of unexpected tags, this paper introduces a two-phase Bloom filter-based missing tag detection (BMTD) protocol. The proposed BMTD exploits Bloom filter in sequence to first deactivate the unexpected tags and then test the membership of the expected tags, thus dampening the interference from the unexpected tags and considerably reducing the detection time. Moreover, the theoretical analysis of the protocol parameters is performed to minimize the detection time of the proposed BMTD and achieve the required reliability simultaneously. In addition, we derive a critical threshold on the unexpected tag size for the execution of first phase in BMTD. Extensive experiments are then conducted to evaluate the performance of the proposed BMTD. The results demonstrate that the proposed BMTD significantly outperforms the state-of-the-art solutions. [ABSTRACT FROM PUBLISHER]

Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio-frequency identification (RFID) applications. However, most, if not all, of the existing estimation mechanisms are proposed for the static case where tag population remains constant during the estimation process, thus leaving the more challenging dynamic case unaddressed, despite the fundamental importance of the latter case on both the theoretical analysis and the practical application. In order to bridge this gap, we devote this paper to designing a generic framework of stable and accurate tag population estimation schemes based on the Kalman filter for both the static and dynamic RFID systems. Technically, we first model the dynamics of RFID systems as discrete stochastic processes and leverage the techniques in the extended Kalman filter and cumulative sum control chart to estimate tag population for both the static and dynamic systems. By employing the Lyapunov drift analysis, we mathematically characterize the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters under which our scheme can stabilize around the real population size with bounded relative estimation error that tends to zero with exponential convergence rate. [ABSTRACT FROM PUBLISHER]

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude–time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted. [ABSTRACT FROM AUTHOR]