Joint Optimization of Wireless Communication and Networked Control Systems

Abstract

Abstract. 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.

Optimized rate allocation for state estimation over noisy channels

“…Especially, x t is not affected by the rate allocation, and the instantaneous distortion functions are separable. Hence, the major challenge lies in deriving a useful expression of the mean-squared error (MSE) for the instantaneous distortion (5). In general, it is hard to formulate closed-form expressions, even in the case of simple uniform quantizers.…”

“…Performance in Fig. 1 is measured by the distortion (5). The distortion is obtained by averaging over 50 IA's and each IA 150 000 samples.…”

“…How to assign bits among the elements of the state vector of the plant, while imposing a constraint on the number of bits over time, can be found in e.g., [4], [5]. In these works, it has often been assumed that bits (rates) are evenly distributed to sensor measurements.…”

Near Optimal Rate Selection for Wireless Control Systems

“…Wireless control co-design has been studied in [17]- [19]. But these works do not consider multi-hop wireless networks.…”

Dynamic Tuning Retransmission Limit of IEEE 802.11 MAC Protocol for Networked Control Systems

“…For the works that improve the NCS performance [3], [4], [5], [6], [7], it is not clear whether they can achieve stability in wireless environment. The other approach is to perform a co-design of the control layer and the communication layer (e.g., network protocols) [8], [9], [10], [11], [12]. While this approach can achieve both stability and optimal performance of NCS, its design inevitably involves too many interactions between the control and the communication layers, which prevents efficient layer abstraction and encapsulation and also hinders broader adoption.…”