This paper studies discrete-time control systems subject to average data-rate limits. We focus on a situation where a noisy linear system has been designed assuming transparent feedback and, due to implementation constraints, a source-coding scheme (with unity signal transfer function) has to be deployed in the feedback path. For this situation, and by focusing on a class of source-coding schemes built around entropy coded dithered quantizers, we develop a framework to deal with average data-rate constraints in a tractable manner that combines ideas from both information and control theories. As an illustration of the uses of our framework, we apply it to study the interplay between stability and average data-rates in the considered architecture. It is shown that the proposed class of coding schemes can achieve mean square stability at average data-rates that are, at most, 1.254 bits per sample away from the absolute minimum rate for stability established by Nair and Evans. This rate penalty is compensated by the simplicity of our approach.

We improve the existing achievable rate regions for causal and for zero-delay source coding of stationary Gaussian sources under an average mean squared error distortion measure. To begin with, we find a closed-form expression for the information-theoretic causal rate-distortion function (RDF) under such distortion measure, denoted by R_c^it(D), for first-order Gauss-Markov processes. R_c^it(D) is a lower bound to the optimal performance theoretically attainable (OPTA) by any causal source code, namely R_c^op(D). We show that, for Gaussian sources, the latter can also be upper bounded as R_c^op(D) ≤ R_c^it(D) + 0.5 log ₂(2πe) bits/sample. In order to analyze R_c^it(D) for arbitrary zero-mean Gaussian stationary sources, we introduce R_c^it̅(D), the information-theoretic causal RDF when the reconstruction error is jointly stationary with the source. Based upon R_c^it̅(D), we derive three closed-form upper bounds to the additive rate loss defined as R_c^it̅(D) - R(D), where R(D) denotes Shannon's RDF. Two of these bounds are strictly smaller than 0.5 bits/sample at all rates. These bounds differ from one another in their tightness and ease of evaluation; the tighter the bound, the more involved its evaluation. We then show that, for any source spectral density and any positive distortion D ≤ σ_x², RU(D) can be realized by an additive white Gaussian noise channel surrounded by a unique set of causal pre-, post-, and feed- back niters. We show that finding such filters constitutes a convex optimization problem. In order to solve the latter, we propose an iterative optimization procedure that yields the optimal niters and is guaranteed to converge to R_c^it̅(D). Finally, by establishing a connection to feedback quantization, we design a causal and a zero-delay coding scheme which, for Gaussian sources, achieves an operational rate lower than R_c^it̅(D) +0.254 and R_c^it̅(D) + 0.754 bits/sample, respectively. This implies that the OPTA among all zero-delay source codes, denoted by R_zd^op(D), is upper bounded as R_zd^op(D) <; R_c^it̅(D) + 1-254 <; R(D) + 1.754 bits/sample.

We study a control architecture for linear time-invariant plants with random disturbances and where a network is placed between the controller output and the plant input. The network imposes a constraint on the expected bit-rate and is affected by random independent and identically distributed (i.i.d.) dropouts. Dropout-rates and acknowledgments of receipt are not available at the controller side. To achieve robustness with respect to i.i.d. dropouts, the controller transmits data packets containing quantized plant input predictions. These are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive stochastic stability results and provide a noise-shaping model of the closed loop system. This model is employed for performance analysis by using rate-distortion theory.

We study feedback control over erasure channels with packet-dropouts. To achieve robustness with respect to packet-dropouts, the controller transmits data packets containing plant input predictions, which minimize a finite horizon cost function. To reduce the data size of packets, we propose to adopt sparsity-promoting optimizations, namely, l ¹ - l ² and l ² -constrained l ⁰ optimizations, for which efficient algorithms exist. We show how to design the tuning parameters to ensure (practical) stability of the resulting feedback control systems when the number of consecutive packet-dropouts is bounded.

Current sound-based practices and systems developed in both academia and industry point to convergent research trends that bring together the field of sound and music Computing with that of the Internet of Things. This article proposes a vision for the emerging field of the Internet of Sounds (IoS), which stems from such disciplines. The IoS relates to the network of Sound Things, i.e., devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In the IoS paradigm, which merges under a unique umbrella the emerging fields of the Internet of Musical Things and the Internet of Audio Things, heterogeneous devices dedicated to musical and nonmusical tasks can interact and cooperate with one another and with other things connected to the Internet to facilitate sound-based services and applications that are globally available to the users. We survey the state-of-the-art in this space, discuss the technological and nontechnological challenges ahead of us and propose a comprehensive research agenda for the field.

We study state estimation via wireless sensors over fading channels. Packet loss probabilities depend upon time-varying channel gains, packet lengths and transmission power levels of the sensors. Measurements are coded into packets by using either independent coding or distributed zero-error coding. At the gateway, a time-varying Kalman filter uses the received packets to provide the state estimates. To trade sensor energy expenditure for state estimation accuracy, we develop a predictive control algorithm which, in an online fashion, determines the transmission power levels and codebooks to be used by the sensors. To further conserve sensor energy, the controller is located at the gateway and sends coarsely quantized power increment commands, only whenever deemed necessary. Simulations based on real channel measurements illustrate that the proposed method gives excellent results.