Linear controller design: limits of performance
Linear controller design: limits of performance
Elements of information theory
Elements of information theory
Stochastic analysis and control of real-time systems with random time delays
Automatica (Journal of IFAC)
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Convex Optimization
Capacity and optimal resource allocation for fading broadcast channels .II. Outage capacity
IEEE Transactions on Information Theory
Optimized rate allocation for state estimation over noisy channels
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Rate allocation for quantized control over noisy channels
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
An overview of wireless networks in control and monitoring
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Dynamic Tuning Retransmission Limit of IEEE 802.11 MAC Protocol for Networked Control Systems
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Near optimal rate selection for wireless control systems
ACM Transactions on Embedded Computing Systems (TECS)
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We consider a linear system, such as an estimator or a controller, in which several signals are transmitted over wireless communication channels. With the coding and medium access schemes of the communication system fixed, the achievable bit rates are determined by the allocation of communications resources such as transmit powers and bandwidths, to different channels. Assuming conventional uniform quantization and a standard white-noise model for quantization errors, we consider two specific problems. In the first, we assume that the linear system is fixed and address the problem of allocating communication resources to optimize system performance. We observe that this problem is often convex (at least, when we ignore the constraint that individual quantizers have an integral number of bits), hence readily solved. We describe a dual decomposition method for solving these problems that exploits the problem structure. We briefly describe how the integer bit constraints can be handled, and give a bound on how suboptimal these heuristics can be. The second problem we consider is that of jointly allocating communication resources and designing the linear system in order to optimize system performance. This problem is in general not convex. We present an iterative heuristic method based on alternating convex optimization over subsets of variables, which appears to work well in practice.