This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. By assuming Gaussian input, ...the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called "interference temperature (IT)" constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.
Wireless sensor networks (WSNs) are an invaluable resource for realizing the vision of the Internet of Things. This paper investigates the energy minimization problem for the joint estimation of a ...noise-corrupted parameter in WSNs, where the sensors send digital signals to a fusion center via orthogonal channels. In particular, each sensor chooses a quantization level, a modulation order, and a transmission bandwidth for its link to the fusion center with the goal to minimize the total energy consumption at the sensors. It is shown that this problem can be approximated as a convex optimization problem and an efficient iterative algorithm is proposed to compute the near-optimal solution. Simulation results are provided to validate the analysis.
Power control in a digital handset is practically implemented in a discrete fashion, and usually, such a discrete power control (DPC) scheme is suboptimal. In this paper, we first show that in a ...Poison-distributed ad hoc network, if DPC is properly designed with a certain condition satisfied, it can strictly work better than no power control (i.e., users use the same constant power) in terms of average signal-to-interference ratio, outage probability, and spatial reuse. This motivates us to propose an N-layer DPC scheme in a wireless clustered ad hoc network, where transmitters and their intended receivers in circular clusters are characterized by a Poisson cluster process on the plane ℝ 2 . The cluster of each transmitter is tessellated into N-layer annuli with transmit power P i adopted if the intended receiver is located at the ith layer. Two performance metrics of transmission capacity (TC) and outage-free spatial reuse factor are redefined based on the N-layer DPC. The outage probability of each layer in a cluster is characterized and used to derive the optimal power scaling law P i ∈ Θ(η i -(α/2) ), with η i as the probability of selecting power Pi and α as the path loss exponent. Moreover, the specific design approaches to optimize Pi and N based on η i are also discussed. Simulation results indicate that the proposed optimal N-layer DPC significantly outperforms other existing power control schemes in terms of TC and spatial reuse.
We consider the joint optimal design of the physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks. The problem of computing ...lifetime-optimal routing flow, link schedule, and link transmission powers for all active time slots is formulated as a non-linear optimization problem. We first restrict the link schedules to the class of interference-free time division multiple access (TDMA) schedules. In this special case, we formulate the optimization problem as a mixed integerconvex program, which can be solved using standard techniques. Moreover, when the slots lengths are variable, the optimization problem is convex and can be solved efficiently and exactly using interior point methods. For general non-orthogonal link schedules, we propose an iterative algorithm that alternates between adaptive link scheduling and computation of optimal link rates and transmission powers for a fixed link schedule. The performance of this algorithm is compared to other design approaches for several network topologies. The results illustrate the advantages of load balancing, multihop routing, frequency reuse, and interference mitigation in increasing the lifetime of energy-constrained networks. We also briefly discuss computational approaches to extend this algorithm to large networks
Stability and reliability are of the most important concern for isolated microgrid systems that have no support from the utility grid. Interval predictions are often applied to ensure the system ...stability of isolated microgrids as they cover more uncertainties and robust control can be achieved based on more sufficient information. In this paper, we propose a probabilistic microgrid energy exchange method based on the Model Predictive Control (MPC) approach to make better use of the prediction intervals so that the system stability and cost efficiency of isolated microgrids are improved simultaneously. Appropriate scenarios are selected from the predictions according to the evaluation of future trends and system capacity. In the meantime, a two-stage adaptive reserve strategy is adopted to further utilize the potential of interval predictions and maintain the system security adaptively. Reserves are determined at the optimization stage to prepare some extra capacity for the fluctuations in the renewable generation and load demand at the operation stage based on the aggressive and conservative level of the system, which is automatically updated at each step. The optimal dispatch problem is finally formulated using the mixed-integer linear programming model and the MPC is formulated as an optimization problem with a discount factor introduced to adjust the weights. Case studies show that the proposed method could effectively guarantee the stability of the system and improve economic performance.
This paper investigates the resource allocation problem for the Gaussian multiple access channel (MAC) with conferencing links, where the two transmitters can talk to each other via wired ...rate-limited channels. Moreover, the two transmitters are powered by a shared energy harvester which captures energy from the environment. We consider both the non-causal (the energy arrival levels at future time slots are known before transmissions) and the causal (only the energy arrival levels of past and present slots are known) energy-harvesting (EH) models. For the non-causal case, we formulate a resource allocation problem over a finite horizon of N time slots to characterize the boundary of the maximum departure region. We then develop the optimal offline power and rate allocation scheme by exploiting the hidden convexity of this problem. Interestingly, it is shown that there exists a maximum transmission rate (named the capping rate) for one of the transmitters. For the causal case, we examine the performance of the greedy scheme, in which the energy is depleted within each slot. In particular, we measure the utility of this scheme against the optimal offline one by competitive analysis, where the competitive ratio of the online greedy scheme, i.e., the maximum ratio between the profits obtained by the offline and online schemes over arbitrary energy arrival profiles, is derived.
We consider the optimal power scheduling problem for the decentralized estimation of a noise-corrupted deterministic signal in an inhomogeneous sensor network. Sensor observations are first quantized ...into discrete messages, then transmitted to a fusion center where a final estimate is generated. Supposing that the sensors use a universal decentralized quantization/estimation scheme and an uncoded quadrature amplitude modulated (QAM) transmission strategy, we determine the optimal quantization and transmit power levels at local sensors so as to minimize the total transmit power, while ensuring a given mean squared error (mse) performance. The proposed power scheduling scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to save power. For the remaining active sensors, their optimal quantization and transmit power levels are determined jointly by individual channel path losses, local observation noise variance, and the targeted mse performance. Numerical examples show that in inhomogeneous sensing environment, significant energy savings is possible when compared to the uniform quantization strategy.
In beamforming, channel state information (CSI) is used to design the beamforming vector (or matrix). Since in a practical system the CSI always needs to be estimated, channel estimation (CE) errors ...are inevitable, which could severely affect the performance of a beamforming scheme. In this paper, we present a novel beamforming method for two-way relay (TWR) systems that is robust against CE errors. The proposed method obtains a sub-optimal solution for the associated non-convex robust optimization problem by solving a set of closed-form linear equations. Simulations show a considerable performance gain over the rank-one relaxation-based semidefinite programming (SDP) solutions, especially for the cases where the relaxed problem becomes infeasible. In addition, there is significant reduction in complexity, making this method very attractive for practical implementation.
Energy-constrained multihop wireless links are considered, where the total power consumption is minimized under given requirements on the end-to-end bit error rate (BER). As multihop transmissions ...are known to be able to save transmission energy in a wireless environment, we study the optimal power scheduling schemes over intermediate hops when the source- relays-destination link can be modeled as cascaded binary symmetric channels. The problem is formulated with an end- to-end BER constraint, and the resulting power consumption is compared with that of the individual link requirement strategy where each hop assigns power under a per-link BER constraint. Results show that the proposed joint power scheduling strategy can achieve a maximum power reduction factor of M in an M-hop route.
In this paper, we study the state-dependent two-user Gaussian interference channel, where the Gaussian distributed state information is non-causally known at both transmitters but known to neither of ...the receivers. We apply the simultaneous encoding scheme and propose an active interference cancellation mechanism, which is a generalized dirty-paper coding technique, to partially eliminate the state effect at the receivers. The corresponding achievable rate region is then derived. We also propose several heuristic schemes for some special cases: the strong interference case, the mixed interference case, and the weak interference case. For the strong and mixed interference cases, numerical results are provided to show that active interference cancellation significantly enlarges the achievable rate region. For the weak interference case, flexible power splitting instead of active interference cancellation improves the performance significantly.
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