Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors

We address the design of opportunistic spectrum access (OSA) strategies that allow secondary users to independently search for and exploit instantaneous spectrum availability. Integrated in the joint design are three basic components: a spectrum sensor that identifies spectrum opportunities, a sensing strategy that determines which channels in the spectrum to sense, and an access strategy that decides whether to access based on imperfect sensing outcomes. We formulate the joint PHY-MAC design of OSA as a constrained partially observable Markov decision process (POMDP). Constrained POMDPs generally require randomized policies to achieve optimality, which are often intractable. By exploiting the rich structure of the underlying problem, we establish a separation principle for the joint design of OSA. This separation principle reveals the optimality of myopic policies for the design of the spectrum sensor and the access strategy, leading to closed-form optimal solutions. Furthermore, decoupling the design of the sensing strategy from that of the spectrum sensor and the access strategy, the separation principle reduces the constrained POMDP to an unconstrained one, which admits deterministic optimal policies. Numerical examples are provided to study the design tradeoffs, the interaction between the spectrum sensor and the sensing and access strategies, and the robustness of the ensuing design to model mismatch.Comment: 43 pages, 10 figures, submitted to IEEE Transactions on Information Theory in Feb. 200

Information reuse in dynamic spectrum access

Dynamic spectrum access (DSA), where the permission to use slices of radio spectrum is dynamically shifted (in time an in different geographical areas) across various communications services and applications, has been an area of interest from technical and public policy perspectives over the last decade. The underlying belief is that this will increase spectrum utilization, especially since many spectrum bands are relatively unused, ultimately leading to the creation of new and innovative services that exploit the increase in spectrum availability. Determining whether a slice of spectrum, allocated or licensed to a primary user, is available for use by a secondary user at a certain time and in a certain geographic area is a challenging task. This requires 'context information' which is critical to the operation of DSA. Such context information can be obtained in several ways, with different costs, and different quality/usefulness of the information. In this paper, we describe the challenges in obtaining this context information, the potential for the integration of various sources of context information, and the potential for reuse of such information for related and unrelated purposes such as localization and enforcement of spectrum sharing. Since some of the infrastructure for obtaining finegrained context information is likely to be expensive, the reuse of this infrastructure/information and integration of information from less expensive sources are likely to be essential for the economical and technological viability of DSA. © 2013 IEEE

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Impact of g-factors and valleys on spin qubits in a silicon double quantum dot

We define single electron spin qubits in a silicon MOS double quantum dot system. By mapping the qubit resonance frequency as a function of gate-induced electric field, the spectrum reveals an anticrossing that is consistent with an inter-valley spin-orbit coupling. We fit the data from which we extract an inter-valley coupling strength of 43 MHz. In addition, we observe a narrow resonance near the primary qubit resonance when we operate the device in the (1,1) charge configuration. The experimental data is consistent with a simulation involving two weakly exchanged-coupled spins with a g-factor difference of 1 MHz, of the same order as the Rabi frequency. We conclude that the narrow resonance is the result of driven transitions between the T- and T+ triplet states, using an ESR signal of frequency located halfway between the resonance frequencies of the two individual spins. The findings presented here offer an alternative method of implementing two-qubit gates, of relevance to the operation of larger scale spin qubit systems

Occupancy Grid Mapping for Personal Radar Applications

Next fifth generation (5G) of mobile wireless communication foresees the use of mm-wave technology to boost communication at an unprecedented scale, thanks to the large available bandwidth [1] . In addition, the move-up in the frequency spectrum allows to include a large number of antennas into a small area, thus enabling their integration into portable devices [2 , 3] . In this way, such a technological perspective can be exploited to add new functionalities in addition to communication. For example, the laser-like beamsteering allowed by massive arrays at mm-wave can be used to automatically scan and reconstruct the topology of the surrounding environment. Such an idea, namely personal radar, has been recently proposed in theory and its feasibility assessed by experiments [4 \u2013 [5]6] . In these works, the performance has been investigated through the adoption of a grid-based mapping approach relying on an extended Kalman-Filter (EKF): the environment has been discretized in a grid of cells whose root-radar cross section (RCS) values constitute the state vector to be estimated starting from the backscattered radar response [7 , 8] . To simplify the analysis, the state of the system has been modeled as a Gaussian random vector whose mean vector and covariance matrix are updated during the mapping process as soon as new measurements are collected [5 , 9] . The main limitation of this model is that the Gaussian assumption does not capture the underlaying bimodal nature of the phenomenon, i.e., each cell is empty or occupied. In laser-based mapping systems, occupancy grid (OG) methods are usually considered to model this bi-modality by exploiting the basic assumption that laser beam illuminates only one cell per time [10] . This is not the case in radio-based radars where the shape of the radiation pattern is such to illuminate an area composed of several cells, thus making existing OG methods not appropriate due to the inherent cross-correlation between cells that is not zero [11]

Bayesian Active Edge Evaluation on Expensive Graphs

Robots operate in environments with varying implicit structure. For instance, a helicopter flying over terrain encounters a very different arrangement of obstacles than a robotic arm manipulating objects on a cluttered table top. State-of-the-art motion planning systems do not exploit this structure, thereby expending valuable planning effort searching for implausible solutions. We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort. Consider the problem of evaluating edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots with limited onboard computation like UAVs. Until now, this challenge has been addressed via laziness i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard, but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters