Ultra sensitive TV detector measurements

The selection of optimum frequency channels by cognitive radio devices is considered challenging, particularly when such devices have to mutually co-exist and also take account of existing users of the spectrum. In addition to the problem of accurate spectrum sensing, there are additional considerations that result because of the dynamic capabilities of the terminals. Particular problems include that of allowing terminals to discover each other when they may be tuned to different frequencies, operating without a common control channel. This work has developed a spectrum access algorithm by which cognitive radio terminals monitor the available spectrum, discover each others’ presence and collaborate to select the optimum communications channel. This is achieved while avoiding interference to incumbent users. In contrast to previous work in this area, the proposed algorithm is of low complexity, requiring the terminals neither to dedicate part of their operational time to spectrum sensing, nor to periodically switch to a control channel. It is thus suitable for implementation in simple terminals. A simulation tool, which models the interaction and behaviour of the terminals, has been produced and used to test the performance of the algorithm in a demanding environment. It was shown that the algorithm permits a high level of data throughput, with protection for incumbent spectrum users.

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.

A scheme that allows LTE-U system to harmoniously coexist with WIFI system in 5GHz unlicensed band is proposed in this paper. On the basis of listen-before-talk (LBT) channel access mechanism and dynamic channel switching strategy, the proposed scheme effectively evades harmful interference or blocking effects, caused by LTE-U system occupying the channel for a long time, to WIFI system. In order to alleviate the fairness problem when only one channel is available, the idle duration of the channel access scheme is optimized to further enhance the coexistence efficiency between LTE-U and WIFI system. Theoretical analysis and experimental results show that the proposed scheme enables the LTE-U and WIFI system to coexist well in the same band and achieve a better system performance.

In order to sustain the historic progress in information processing, transmission, and storage, concurrent integration of heterogeneous functionality and materials with fine granularity is clearly imperative for the best connectivity, system performance, and density metrics. In this paper, we review recent developments in heterogeneous integration of epitaxial nanostructures for their applications toward our envisioned device-level heterogeneity using computing nanofabrics. We first identify the unmet need for heterogeneous integration in modern nanoelectronics and review state-of-the-art assembly approaches for nanoscale computing fabrics. We also discuss the novel circuit application driver, known as Nanoscale Application Specific Integrated Circuits (NASICs), which promises an overall performance-power-density advantage over CMOS and embeds built-in defect and parameter variation resilience. At the device-level, we propose an innovative cross-nanowire field-effect transistor (xnwFET) structure that simultaneously offers high performance, low parasitics, good electrostatic control, ease-of-manufacturability, and resilience to process variation. In addition, we specify technology requirements for heterogeneous integration and present two wafer-scale strategies. The first strategy is based on ex situ assembly and stamping transfer of pre-synthesized epitaxial nanostructures that allows tight control over key nanofabric parameters. The second strategy is based on lithographic definition of epitaxial nanostructures on native substrates followed by their stamping transfer using VLSI foundry processes. Finally, we demonstrate the successful concurrent heterogeneous co-integration of silicon and III-V compound semiconductor epitaxial nanowire arrays onto the same hosting substrate over large area, at multiple locations, with fine granularity, close proximity and high yield.

We study algorithms for wireless spectrum access of $n$ communication requests when interference conditions are given by the Rayleigh-fading model. This model extends the recently popular deterministic interference model based on the signal-to-interference-plus-noise ratio (SINR) using stochastic propagation to address fading effects observed in reality. We consider worst-case approximation guarantees for the two standard problems of capacity maximization (maximize the expected number of successful transmissions in a single slot) and latency minimization (minimize the expected number of slots until all transmissions were successful). Our main result is a generic reduction of Rayleigh fading to the deterministic SINR model. It allows to apply existing algorithms for the non-fading model in the Rayleigh-fading scenario while losing only a factor of O(logast n) in the approximation guarantee. This way, we obtain the first approximation guarantees for Rayleigh fading and, more fundamentally, show that non-trivial stochastic fading effects can be successfully handled using existing and future techniques for the non-fading model. Using a more detailed argument, a similar result applies even for distributed and game-theoretic capacity maximization approaches. For example, it allows to show that regret learning yields an O(log* n)-approximation with uniform power assignments. Our analytical treatment is supported by simulations illustrating the performance of regret learning and, more generally, the relationship between both models.

In this study, we design and implement two algorithms for dynamic spectrum access (DSA) that are based on survival analysis. They use a non-parametric estimate of the cumulative hazard function to predict the remaining idle time available for secondary transmission subject to the constraint of a preset probability of successful completion. To show that the algorithms are effective in real-world scenarios even at fine time scales, we evaluate their performance using data collected from an LTE band to model primary user activity. The algorithms are run in different configurations, i.e., they are trained and run on a few combinations of data sets. Our results show that as long as the cumulative hazard functions are fairly similar across datasets, the algorithms can be trained on one day's dataset and run on that of another day's without any significant degradation of performance. The algorithms achieve fairly high white space utilization and have a measured probability of interference which always stays below the preset threshold.

A stochastic multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. In contrast to prior work, it is assumed that rewards can be non-zero even under collisions, thus allowing for the number of users to be greater than the number of channels. The proposed algorithm consists of an estimation phase and an allocation phase. It is shown that if every user adopts the algorithm, the system wide regret is order-optimal of order $O(\log T)$ over a time-horizon of duration $T$. The regret guarantees hold for both the cases where the number of users is greater than or less than the number of channels. The algorithm is extended to the dynamic case where the number of users in the system evolves over time, and is shown to lead to sub-linear regret.

One of the ways to harness the radio bands that are not used by their primary owners, is to allow opportunistic use of the bands to be used by secondary users such that they do not interfere with the primary users. Most often, multiple secondary users contend to acquire the bands that are available. Proper scheduling techniques can resolve such contentions. However, all scheduling techniques have their own pros and cons. In this paper, we propose scheduling algorithms for dynamic spectrum access (DSA) networks that allocate channels by exploiting favorable instantaneous channel conditions of secondary users and the transmission activities of primary users. We propose two scheduling algorithms- the first one focuses on maximizing the throughput and the second one focuses on social welfare. Scheduling is performed at the beginning of each super-frame where appropriate slots(s) are assigned to secondary users. In order to better utilize the channels, multiple secondary users are allocated the same channel in the same time slot as long as there is no interference between them. However, finding such conflict-free independent set of secondary users is a NP-complete problem. We use the signal to interference and noise ratio (SINR) to find the independent sets for throughput maximization, while we use the allocation history for the achieving fairness. Using simulation, we show the performances of the proposed algorithms in terms of throughput, number of slots allocated, and fairness achieved among the users.

Industrial IoT (IIoT) is critical for industrial infrastructure modernization and digitalization. Therefore, it is of utmost importance to provide adequate protection of the IIoT system. A modern IIoT system usually consists of a large number of devices that are deployed in multiple locations and owned/managed by different entities who do not fully trust each other. These features make it harder to manage the system in a coherent manner and utilize existing security mechanisms to offer adequate protection. The emerging blockchain technology provides a powerful tool for IIoT system management and protection because the IIoT nature of distributed deployment and involvement of multiple stakeholders fits the design philosophy of blockchain well. Most existing blockchain construction mechanisms are not scalable enough and too heavy for an IIoT system. One promising way to overcome these limitations is utilizing hardware based trusted execution environment (TEE) in blockchain construction. However, most of the existing works on this direction do not consider the characteristics of IIoT devices (e.g., fixed functionality and limited supply) and face several limitations when they are applied for IIoT system management and protection, such as high energy consumption, single root-of-trust, and low decentralization level. To mitigate these challenges, we propose a novel field programmable gate array (FPGA) based blockchain system. It leverages the FPGA to build a simple but efficient TEE for IIoT devices, and removes the single root-of-trust by allowing all stakeholders to participate in the management of the devices. The FPGA based blockchain system shifts the computation/storage intensive part of blockchain management to more powerful computers but still involves the IIoT devices in the block construction to achieve a high level of decentralization. We implement the major FPGA components of the design and evaluate the performance of the whole system with a simulation tool to demonstrate its feasibility for IIoT applications.

Monitoring human movements using wireless sensory devices promises to revolutionize the delivery of healthcare services. Such platforms use inertial information of their subjects for motion analysis. Potentially, each action or disease can be discovered by collaborative processing of sensor data from multiple locations on the body. This functionality is provided by a Body Sensor Network (BSN), which consists of several wireless sensor nodes positioned on different parts of the body. In spite of the revolutionary potential of this platform, power requirements and wearability have limited the commercialization of these systems. In this paper, we present an energy-efficient communication model for BSN applications which uses buffers to limit communication to short bursts, decreasing power usage and simplifying the communication. We formulate an optimization problem to reduce transmissions among sensor nodes and present an ILP-based solution and a fast greedy heuristic algorithm. We show that despite the decreased transmission efficiency, our greedy algorithm can be adopted for fast allocation of buffers in real-time. We experimentally compare the performance of both of the proposed approaches to the performance of an unbuffered system. Our results demonstrate that ILP and greedy solutions can reduce the amount of transmissions by an average factor of 70 and 41, respectively.

In this paper, we proposed a novel sensing scheme so-called Transmitter-Receiver Cooperative Sensing in a MIMO cognitive network by combining sensing information of both sides. Compared with most of the existing sensing schemes which simply use listen-before talk (LBT) only at the transmitter side, the proposed scheme can further improve the system performance by jointly feedback the Channel State Information (CSI) and the Sensing information (SI) from the receiver. However, the quantization impact on the sensing performance and the channel capacity as well as the interference to primary network should be considered in the case of limited feedback. Here a tradeoff criterion is proposed to allocate bits for CSI and SI so as to maximize the MIMO link capacity while meeting the interference constraints. Simulation results are given to verify the effectiveness of the proposed scheme.

In this paper, we explore the potential of developing an interweave cognitive radio (CR) system based on a hierarchical wide-band two-dimensional spread multi-carrier directed-sequenced code division multiple access (MC-DS-CDMA). First, a small CR cell is embedded in a large primary cell such that the spatial separation gives a chance to share the spreading code resources with the primary system. Second, the white, gray and black spreading codes are defined to facilitate the management of the spreading code resources. Also, a new multiple access interference (MAI) coefficient is derived to quantize the amount of intra- and inter-system interference. Third, based on the MAI coefficient, a dynamic spectrum access (DSA) algorithm, including the code assignment/reassignment, transmission power control and call admission control mechanisms, is proposed to efficiently utilize the white and gray spreading codes without damaging the primary system. The simulation results show that compared with the the case of using only the white spreading codes, some extra 53% increase in call admissions and 13% decrease in call droppings can be attained by using the gray spreading codes properly.

As a new technology, deep reinforcement learning (DRL) has been applied in many fields of wireless communication in recent years. In this paper, we propose a centralized dynamic spectrum access (DSA) scheme based on DRL and convolutional neural network (CNN). The traditional distributed DSA scheme encounters two important issues, namely, interference suppression for primary users (PU) and interference coordination for secondary users (SU). In order to cope with these two issues, a centralized DSA scheme implemented with deep convolution Q-network is proposed in this paper. Operating with the proposed centralized deep convolution Q-network spectrum access (CDCQSA) algorithm, a central unit (CU) uses DRL to unify the spectrum access actions in the network, allowing multiple SU to share spectrum resources with multiple PU without requiring any prior information. Simulation results show that this proposed scheme improves the channel utilization and reduces the conflict probability between PU and SU while avoiding the collisions between SU as well.

The combined influence of multiple ray arrivals and sensor location offsets on the performance of a broadband cross‐correlation system is investigated. The system consists of three sensors that are nominally positioned on a horizontal line. Source location parameters are determined from time‐delay estimates obtained by cross‐correlation techniques. An analytical model for the prediction of localization bias errors due to multipath and to offsets from nominal sensor positions is discussed. This model is applicable to an oceanic medium with a depth‐dependent sound‐speed profile. Results are presented for a variety of multipath conditions, such as those associated with direct path, surface duct, bottom bounce, and convergence zone types of propagation. The sensitivity of localization errors to variations in environmental parameters, such as sea state or bottom‐reflection properties, is also investigated. It is demonstrated that multipath, especially when combined with sensor position offsets, can have a significant impact on system performance.

The deployment of long term evolution (LTE) in the unlicensed spectrum (LTE-U) has been gaining significant industry momentum in recent months. The US 5-GHz Unlicensed National Information Infrastructure (UNII) bands that are currently under consideration for LTE deployment in the unlicensed spectrum contain only a limited number of 20 MHZ channels. Thus, in a dense multi-operator deployment scenario, one or more LTE-U small cells have to coexist and share the same 20 MHz unlicensed channel with each other and with the incumbent Wi-Fi. In this paper, we present the scenario and demonstrate that in the absence of an explicit interference mitigation mechanism, there will be a significant degradation in the overall LTE-U system performance for LTE-U cochannel coexistence in countries that do not mandate listen-before-talk (LBT) requirements. We then present the unlicensed spectrum intercell interference co-ordination (usICIC) mechanism as a time-domain multiplexing technique for interference mitigation for the sharing of an unlicensed channel by multioperator LTE-U small cells. Through extensive simulation results, we also demonstrate that our proposed usICIC mechanism will result in 40% or more improvement in overall LTE-U system performance.