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Wireless transmission in future cyber-physical systems

Time: Mon 2021-11-29 13.00

Location: F3, Lindstedsvägen 26, Stockholm

Language: English

Subject area: Electrical Engineering

Doctoral student: Hasan Basri Celebi , Teknisk informationsvetenskap

Opponent: Professor Petar Popovski, Aalborg University, Denmark

Supervisor: Professor Mikael Skoglund, Teknisk informationsvetenskap


This thesis studies some fundamental aspects of wireless communication in future cyber-physical systems with special emphasis on constraints on computational complexity and channel state information (CSI) at the transmitter. First, major challenges in designing a suitable wireless communication solution for future cyber-physical systems are initially discussed. A comprehensive overview of the state-of-the-art wireless communication standards, which are suitable for future cyber-physical applications, is presented and representative comparisons on some of the most common wireless communication technologies including 5G, the next generation of the wireless technologies, are provided. Next, we focus on ultra-reliable low-latency communication (URLLC), which is highly relevant for mission-critical applications, and list the challenges of URLLC. 

Next, a general background on the channel capacity is given. We then study the theoretical limits on the transmission of packets in URLLC. However, since theoretical analysis with stringent latency requirements in URLLC cannot rely on conventional information-theoretic results, which assume asymptotically large blocklengths, we introduce the maximum achievable rates in the non-asymptotical regime, named as the finite blocklength regime. Based on these results we list several encoder-decoder pairs that perform close to the bounds in the finite blocklength regime. 

The next part of the thesis is devoted to the problem of latency and reliability trade-off in URLLC in the presence of decoding complexity constraints. We consider linear block encoded codewords transmitted over a binary-input AWGN channel and decoded with ordered-statistic (OS) decoder. We first investigate the performance of OS decoders as a function of decoding complexity and propose an empirical model that accurately quantifies the corresponding trade-off. Based on the proposed model, several optimization problems including minimization of aggregate latency, minimization of per-information-bit energy, and maximization of the total number of transmitted information bits, which are relevant to the design of URLLC systems, are formulated and solved.  It is shown that the decoding time has a drastic effect on the design of URLLC systems when constraints on decoding complexity are considered. By extending the analysis on latency and reliability trade-off in URLLC in the presence of decoding complexity constraint, we next investigate the optimal selection of transmission rate and power pair, while satisfying the constraints. For this purpose, a multi-objective optimization problem (MOOP) is formulated. In order to assess the overall performance among several Pareto-optimal transmission pairs, two scalarization methods are investigated. To exemplify the importance of the MOOP, a case study on a battery-powered communication system is provided. It is shown that, compared to the classical fixed rate-power transmissions, the MOOP provides the optimum usage of the battery and increases the energy efficiency of the communication system while maintaining the constraints.

The last part of the thesis deals with the constraint on the CSI at the transmitter. In this part, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect CSI and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them and then present iterative algorithms that successfully exploit the system parameters for both cases. It is shown that significant improvement can be achieved even with coarsely quantized feedback schemes. On the other hand, it is also shown that, the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements.