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This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy. [ABSTRACT FROM PUBLISHER]

Index tracking is a popular passive portfolio management strategy that aims at constructing a portfolio that replicates or tracks the performance of a financial index. The tracking error can be minimized by purchasing all the assets of the index in appropriate amounts. However, to avoid small and illiquid positions and large transaction costs, it is desired that the tracking portfolio consists of a small number of assets, i.e., a sparse portfolio. The optimal asset selection and capital allocation can be formulated as a combinatorial problem. A commonly used approach is to use mixed-integer programming (MIP) to solve small sized problems. Nevertheless, MIP solvers can fail for high-dimensional problems while the running time can be prohibiting for practical use. In this paper, we propose efficient and fast index tracking algorithms that automatically perform asset selection and capital allocation under a set of general convex constraints. A special consideration is given to the case of the nonconvex holding constraints and to the downside risk tracking measure. Furthermore, we derive specialized algorithms with closed-form updates for particular sets of constraints. Numerical simulations show that the proposed algorithms match or outperform existing methods in terms of performance, while their running time is lower by many orders of magnitude. [ABSTRACT FROM PUBLISHER]

In this paper, we study the target tracking problem in a wireless sensor network. A sensor receives a measurement from an energy emitting target and employs binary quantization to the received measurement to generate its decision. A sinusoidal waveform with a certain duration is then used to transmit the sensor decision to the fusion center (FC). All sensor decisions are transmitted to the FC over erroneous wireless channels based on a time division multiple access scheme. We introduce the proportional time allocation (PTA) algorithm where at each time step of tracking, PTA jointly determines the sensors binary quantization thresholds and their time allocations devoted for the transmissions of binary sensor decisions. Simulation results show that, PTA optimally and dynamically distributes the available transmission time among the sensors near the target so that the decisions of such sensors become less subject to channel errors, and turns off the non-informative sensors located far away from the target. Hence, PTA both saves from the number of sensors transmitting to the FC and provides better estimation performance as compared to ad hoc equal time allocation approaches. [ABSTRACT FROM PUBLISHER]

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for 1) the maximization of the sum capacity of all users; 2) the maximization of the worst user capacity; and 3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results. [ABSTRACT FROM PUBLISHER]

This paper proposes a framework for the artificial noise assisted secure transmission in multiple-input, multiple-output, multiple antenna eavesdropper (MIMOME) wiretap channels in frequency-division duplexed (FDD) systems. We focus on a practical scenario that only the eavesdroppers’ channel distribution information (CDI) is available and the imperfect channel state information (CSI) of the legitimate receiver is acquired through training and analog feedback. By taking explicitly into account the signaling overhead and training power overhead incurred by channel estimation and feedback, we define the achievable effective ergodic secrecy rate (ESR), and investigate a joint power allocation and training overhead optimization problem for the maximization of effective ESR. We first derive a deterministic approximation for the achievable effective ESR which facilitates the joint optimization. Then, efficient iterative algorithms are proposed to solve the considered nonconvex optimization problem. In particular, in the high-SNR regime, a block coordinate descent method (BCDM) is proposed to handle the joint optimization. In the low-SNR regime, we transform the problem into a sequence of geometric programmings (GPs) and locate its Karush–Kuhn–Tucker (KKT) solution using the successive convex approximation (SCA) method. For the general case of SNR, we maximize the lower bound of the achievable effective ESR. Simulation results corroborate the theoretical analysis and illustrate the secrecy performance of the proposed secure transmission scheme. [ABSTRACT FROM AUTHOR]

In this paper, the iterative water-filling (IWF) algorithm is considered when the noise-plus-interference levels fed back from the receivers are quantized. A sufficient condition for this algorithm to have a unique Nash equilibrium is derived. This condition is shown to approach the corresponding condition for standard IWF when the quantization resolution or the transmission power are sufficiently high. [ABSTRACT FROM AUTHOR]

Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancelation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation. [ABSTRACT FROM PUBLISHER]

The present paper is concerned with a sensor network, where each sensor is modeled by either a linear or nonlinear sensing system. These sensors team up in observing either static or dynamic random targets and transmit their observations through noisy communication channels to a fusion center (FC) for locating/tracking the targets. Physically, the network is limited by energy resource. According to the available sum power budget, we develop a novel technique for power allocation to the sensor nodes that enables the FC produce the best linear estimate in terms of the mean square error (MSE). Regardless of whether the sensor measurements are linear or nonlinear, the targets are scalar or vectors, static or dynamic, the corresponding optimization problems are shown to be semidefinite programs (SDPs) of tractable optimization and thus are globally and efficiently solved by any existing SDP solver. In other words, new tractably computational algorithms of distributed Bayes filtering are derived with full multisensor diversity achieved. Intensive simulation shows that these algorithms clearly outperform previously known algorithms. [ABSTRACT FROM PUBLISHER]

The goal of this paper is to propose a bio-inspired radio access mechanism for cognitive networks mimicking the behavior of a flock of birds swarming in search for food in a cohesive fashion without colliding with each other. The equivalence between swarming and radio resource allocation is established by modeling the interference distribution in the resource domain, e.g., frequency and time, as the spatial distribution of food, while the position of the single bird represents the radio resource chosen by each radio node. The swarming mechanism is enforced by letting every node allocate its resources (power/bits) in the time-frequency regions where the interference is minimum (the food density is maximum), avoiding collisions with other nodes (birds), yet limiting the spread in the time-frequency domain (i.e., maintaining the swarm cohesion). The solution is given as the distributed minimization of a functional, borrowed from social foraging swarming models, containing the average interference plus repulsion and attraction terms that help to avoid conflicts and maintain cohesiveness, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. Within this framework the informativeness of a delayed (out-of-sequence) measurement (OOSM) is estimated using a lightweight procedure and uninformative measurements are immediately discarded. The framework requires the identification of a threshold that separates informative from uninformative; this threshold selection task is formulated as a constrained optimization problem, where the goal is to minimize state estimation error whilst controlling the computational requirements. We develop an algorithm that provides an approximate solution for the optimization problem. Simulation experiments provide an example where the proposed framework processes less than 40% of all OOSMs with only a small reduction in state estimation accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a wireless amplify-and-forward relay network with one relay node and multiple source-destination pairs/users and propose a pricing framework that enables the relay to set prices to maximize either its revenue or any desirable system utility. Specifically, depending on the quality of the received signals, the relay sets prices and correspondingly charges the users utilizing its resources for their transmissions. The price is determined in such a way that the relay's revenue or system utility is maximized. Given the specified price, the users competitively employ the relay node to forward their signals. We model each user as a rational player, which aims at maximizing its own net utility through power allocation, and analyze the competition among the users within the framework of noncooperative game theory. It is shown that, in the game played by the users, there always exists a unique pure Nash equilibrium point that can be achieved through distributed iterations. Next, subject to the availability of complete information about the users at the relay, we propose a low-complexity uniform pricing algorithm and an optimal differentiated pricing algorithm, in which the relay either charges the users at a suboptimal uniform price or charges different users at different prices. We also show that, by applying the differentiated pricing algorithm that enforces the users to transmit at certain power levels, any system utility can be maximized. Extensive simulations are conducted to quantify the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, low-complexity joint power assignment algorithms are developed for multi-source multi-destination relay networks where multiple sources share a common relay that forwards all received signals simultaneously to destinations. In particular, we consider the following power optimization strategies: (i) Minimization of the total transmission power of the sources and the relay under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirement of each source-destination pair is satisfied, and (ii) Maximization of the minimum SINR among all source-destination pairs subject to any given total power budget. Both optimization problems involve K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop a methodology that allows us to obtain an asymptotically tight approximation of the SINR and reformulate the original optimization problems to single-variable optimization problems, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization schemes are scalable and lead to power assignment algorithms that exhibit the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, we apply the methodology that we developed to solve a related max-min SINR based optimization problem in which we determine power assignment for the sources and the relay to maximize the minimum SINR among all source-destination pairs subject to any given total power budget. Extensive numerical studies illustrate and validate our theoretical developments. [ABSTRACT FROM AUTHOR]

Distributed network utility maximization (NUM) has received an increasing intensity of interest over the past few years. Distributed solutions (e.g., the primal-dual gradient method) have been intensively investigated under fading channels. As such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the convergence behavior and tracking errors of the iterative primal-dual scaled gradient algorithm (PDSGA) with dynamic scaling matrices (DSC) for solving distributive NUM problems under time-varying fading channels. We shall also study a specific application example, namely the multicommodity flow control and multicarrier power allocation problem in multihop ad hoc networks. Our analysis shows that the PDSGA converges to a limit region rather than a single point under the finite state Markov chain (FSMC) fading channels. We also show that the order of growth of the tracking errors is given by \cal O\left(\bar T/\bar N\right), where \bar T and \bar N are the update interval and the average sojourn time of the FSMC, respectively. Based on this analysis, we derive a low complexity distributive adaptation algorithm for determining the adaptive scaling matrices, which can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed dynamic scaling matrix algorithm over several baseline schemes, such as the regular primal-dual gradient algorithm. [ABSTRACT FROM AUTHOR]

This paper studies optimal bandwidth and power allocation in a cognitive radio network where multiple secondary users (SUs) share the licensed spectrum of a primary user (PU) under fading channels using the frequency division multiple access scheme. The sum ergodic capacity of all the SUs is taken as the performance metric of the network. Besides the peak/average transmit power constraints at the SUs and the peak/average interference power constraint imposed by the PU, total bandwidth constraint of the licensed spectrum is also taken into account. Optimal bandwidth allocation is derived in closed-form for any given power allocation. The structures of optimal power allocations are also derived under all possible combinations of the aforementioned power constraints. These structures indicate the possible numbers of users that transmit at nonzero power but below their corresponding peak powers, and show that other users do not transmit or transmit at their corresponding peak powers. Based on these structures, efficient algorithms are developed for finding the optimal power allocations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the base station controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in each cluster are adaptive to both intracluster channel state information (CCSI) and intracluster queue state information (CQSI), and computed at each cluster manager (CM) over a shorter time scale. We show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost constrained partially observed Markov decision process (CPOMDP). By exploiting the special problem structure, we derive an equivalent Bellman equation in terms of pattern selection Q-factor to solve the CPOMDP. To address the distributed requirement and computational complexity, we approximate the pattern selection Q-factor by the sum of per-cluster potential functions and propose a novel distributed online learning algorithm to estimate them distributedly. We show that the proposed distributed online learning algorithm converges almost surely. By exploiting the birth-death structure of the queue dynamics, we further decompose the per-cluster potential function into the sum of per-cluster per-user potential functions and formulate the instantaneous power allocation as a per-stage QSI-aware interference game played among all the CMs. The proposed QSI-aware simultaneous iterative water-filling algorithm (QSIWFA) is shown to achieve the Nash equilibrium (NE). [ABSTRACT FROM AUTHOR]

In recent years an increasing effort was made to reduce the energy consumption in digital subscriber line equipment. Dynamic spectrum management (DSM) has been identified as one promising method to achieve energy-efficiency in discrete multitone based systems. An open research question is how to ensure system robustness when applying highly optimized energy-efficient spectrum management. In this paper, we study the problem of uncertainty in crosstalk noise and parameters, the knowledge of which is indispensable for many DSM algorithms. We introduce robust optimization for spectrum balancing as a technique to achieve feasibility of the optimal power-allocation under a deterministic parameter uncertainty model. This can be seen as an extension of current schemes for spectrum balancing. As a special case we consider the simple strategy of scaling the crosstalk parameters to theirworst-case values, which corresponds to a specific uncertainty model and entails no changes to current DSM algorithms. Finally, we quantify the benefit in worst-case performance and the price in terms of energy by simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose two myopic sensor scheduling algorithms for target tracking scenarios in which there is a tradeoff between tracking performance and sensor-usage costs. Specifically, we consider the problem of activating the lowest cost combination of at most L sensors that maintains a desired squared-error accuracy in the target's position estimate. For sensors that provide position information only, we develop a binary (0-1) mixed integer programming formulation for the scheduling problem and solve it using a linear programming relaxation-based branch-and-bound technique. For sensors that provide both position and velocity information, we pose the scheduling problem as a binary convex programming problem and solve it using the outer approximation algorithm. We apply our scheduling procedures in a network of sensors where the sensor-usage costs correspond to network energy consumption. Our simulation results demonstrate that scheduling using binary programming allows us to obtain optimal solutions to scheduling involving up to 50-70 sensors typically in the order of seconds. [ABSTRACT FROM AUTHOR]