Cooperative spectrum sensing has been studied to combat the hidden terminal problem by exploiting the spatial diversity in cognitive radio (CR) networks. This paper concerns blind cooperative ...spectrum sensing with soft fusion, where the a priori knowledge of channels and primary signals is unavailable, and soft information is transmitted from each secondary user (SU) to a fusion center for detection. We first introduce the Quade test to design a blind detector. Then, a new detector with both lower computational complexity and lower overhead is derived, where only the estimated power and the variance of the instantaneous power at each SU are required at the fusion center. The analytical expressions for the detection performance, in terms of false-alarm probability and detection probability, are derived for the proposed detector. Simulation results are provided to validate the theoretical analyses and demonstrate the superior performance of proposed detector compared to the state-of-the-art detectors. It is also shown that, with the increase of the number of hidden terminals in the CR, the proposed detector can maintain high detection performance while the conventional detectors exhibit rapid performance degradation.
Named Data Networking (NDN) is regarded as one of the promising architectures of future Internet, in which every packet has a name and packet forwarding is based on lookup of name other than IP ...address. Scalable fast packet forwarding is always a challenge in NDN. In this letter, a MPLS-like label switching mechanism is proposed, called Name Label Switching (NLS), which uses fixed-length label swapping replacing unbounded name lookup at core nodes and caches data only at edge nodes. The NLS node architecture and forwarding process were presented, and its forwarding performance was analyzed and evaluated by simulation. It showed that NLS can improve the forwarding performance significantly. Using NLS, about 37% of the data response time and about 70% of the Interest packets processing time at core nodes can be saved, compared to native NDN.
Age-of-information (AoI) based minimization problems have been widely considered in Internet-of-Things (IoT) networks with the settings of multi-source single-channel systems and multi-source ...multi-channel systems. Most existing works are limited to either the case of identical multi-channel or independent sources. In this paper, we study this problem under the identical and non-identical multi-channel, as well as the correlated sources setting. This correlation defines the case when updating a source's AoI; others correlated to this one will also reveal partial information. To tackle this AoI-based minimization problem, we formulate it as a correlated restless multi-armed bandit (CRMAB) problem. By decoupling the CRMAB problem into <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> independent single-armed bandit problems, we derive the closed-form expressions of the generalized Whittle index (GWI) and the generalized partial Whittle index (GPWI) under the identical channel and the non-identical channel settings, respectively. Then, we put forth the GWI-based and GPWI-based scheduling policies to solve this AoI-based minimization problem. In addition, we provide two lower numerical performance bounds for the proposed policies by solving the relaxed Lagrange problem of the decoupled CRMAB. Numerical results show that the proposed policies can achieve these lower bounds and outperform the state-of-the-art scheduling policies. Compared with the case of independent sources, the performance of the proposed policies in the case of correlated sources improves significantly, especially in high-density networks.
As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple challenges. This paper ...studies quality of experience (QoE)-oriented resource allocation in MEC while considering user diversity, limited resources, and the complex relationship between allocated resources and user experience. We introduce a closed-loop online resource allocation (CORA) framework to tackle this problem. It learns the objective function of resource allocation from the historical dataset and updates the learned model using the online testing results. Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem. Thereafter, we put forth a data-driven optimal online queue resource allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm for homogenous and heterogeneous user cases, respectively. Moreover, we provide a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive experiments to evaluate the proposed algorithms using the synthesis and YouTube datasets. Numerical results validate the theoretical analysis and demonstrate that the user complaint rate is reduced by up to 100% and 18% in the synthesis and YouTube datasets, respectively.
Due to the harsh propagation environment, limited bandwidth, and constrained battery life, transmission efficiency is a crucial issue for underwater acoustic (UWA) communications. This paper studies ...the link adaptation problem of a single UWA link by jointly selecting the transmission frequency and data rate. Since the current UWA channel lacks a universal model, we formulate this joint selection problem as a model-based stochastic multi-armed bandit (SMAB) problem. Thereafter, we propose three algorithms to solve this model-based SMAB problem under the settings of the stationary channel, non-stationary channel, and large arm (i.e., frequency and rate pair) space. For the stationary channel, we propose a unimodal objective-based Thompson sampling (UO-TS) algorithm by exploiting the unimodal feature of the objective function. For the non-stationary channel, we put forth a hybrid change detection UO-TS (HCD-UO-TS) algorithm based on the features of the unimodal objective function and non-stationary channel. For the large arm space, we propose an iterative boundary-shrinking TS (IBS-TS) algorithm by using the logistic regression-based arm classification model. These algorithms are all data-driven and have low complexity and a fast convergence rate. In addition, we derive an upper regret bound for the UO-TS algorithm. Numerical results show that the proposed algorithms outperform the state-of-the-art bandit algorithms and are not sensitive to the arm space.
Probiotic fermented fruit extract is a new kind of food with potential health care efficacy. In this study, transformation of bioactive substances in Lactic acid bacteria (LAB) and yeast fermented ...kiwifruit extract were researched. The highest levels of total polyphenol, superoxide dismutase (SOD) were found in Lactobacillus paracasei LG0260 fermented kiwifruit extract with 2.31 ± 0.06 mgGAE/g, 369 ± 12.73 U.mL−1, respectively. LAB fermented kiwifruit extract dramatically had a highest vitamin C (VitC) concentration during the fermentation, while yeast and natural fermented samples were decline steadily. Another interesting founding, a highest level of γ-aminobutyric acid (GABA) was found in Saccharomyces cerevisiae J2861 fermented kiwifruit extract with 24.132 ± 1.01 μg/mL. Furthermore, the main organic acids in fresh kiwifruit and yeast fermented kiwifruit extract were citric acid and malic acid. However, lactic acid and citric acid were the main organic acids in LAB fermented kiwifruit extract. A total of 43 kinds of flavor compounds in fresh kiwifruit and 88 kinds of flavor compounds in fermented kiwifruit extract were identified. Esters and alcohols in fermented kiwifruit system increased by the fermentation of selected bacteria which improved the taste and flavor.
•Probiotic fermented kiwifruit extract is a new kind of health care efficacy food.•The evolution of biochemical components in long-term fermented system was proved.•The change of organic acids in fermented kiwifruit extract was revealed.•The flavor compounds of different fermented kiwifruit extracts were compared.
This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface ...(RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information of the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the <inline-formula> <tex-math notation="LaTeX">\epsilon </tex-math></inline-formula>-greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e., <inline-formula> <tex-math notation="LaTeX">\mathcal {O}(\log ^{1+\delta }_{2} T) </tex-math></inline-formula>, increasing logarithmically with the time horizon <inline-formula> <tex-math notation="LaTeX">T </tex-math></inline-formula>. Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit the high-density networks.
Location privacy protection (LPP) has become a key concern during mobile crowdsourcing (MCS) task allocation. Existing LPP mechanisms for MCS applications mainly focus on 2-D plane scenarios or ...directly apply 2-D techniques into 3-D space scenarios, leaving the height dimension of 3-D geolocation vulnerable to privacy breaches. To facilitate the LPP in 3-D MCS, we propose a learning-based geo-perturbation mechanism using 3-D geo-indistinguishability (3D-GI). In this mechanism, we first define an optimization objective to balance location privacy and MCS server profit, making it adaptable to different types of MCS applications. Then, we adopt the asynchronous advantage Actor–Critic (A3C) algorithm to design a reinforcement learning (RL)-based approach without knowing the accurate system and attack models. This approach enables us to derive the optimal perturbation policy in continuous policy space and accelerates the learning speed using asynchronous multithread training. Simulation results demonstrate that the proposed mechanism can better balance location privacy and server profit in 3-D MCS applications compared to existing benchmarks.
This article studies the network-level throughput of a full-duplex (FD)-enabled CSMA network, considering the link's transmit power (TP) control, carrier-sensing threshold (CST) adjustment, and ...logarithm access intensity (LAI) adaptation. With the FD technique, a transmitter-receiver pair can transmit and receive simultaneously in the same frequency band. We aim to find the best combination of TP, CST, and LAI for each link to maximize the FD-CSMA network throughput. However, adjusting each link's TP and CST will change the network's carrier-sensing relation or contention graph, consequently leading to a computationally intractable network optimization problem. On the other hand, it is difficult to jointly optimize these three parameters in a fully distributed network. To overcome these, we first decompose this network optimization problem into two subproblems: 1) a joint control and scheduling problem in the transport- and media access control (MAC)-layer and 2) a parameter selection problem in the PHY-layer. Then, the multiplayer multiarmed bandit (MPMAB) framework has been introduced to address this problem by solving the two subproblems alternately. We put forth a fully distributed algorithm, named the stochastic and adversarial optimal FD-CSMA (SAO-FD-CSMA) algorithm, to solve the MPMAB problem by taking advantage of the optimization tool and the bandit theory. The numerical results show that the proposed algorithm outperforms the state-of-the-art bandit algorithms and can improve the network throughput by 43% compared with the random selection method.
