Stringent constraints on both reliability and latency must be guaranteed in ultra-reliable low-latency communication (URLLC). To fulfill these constraints with computationally constrained receivers, such as low-budget IoT receivers, optimal transmission parameters need to be studied in detail. In this paper, we introduce a multi-objective optimization framework for the optimal design of URLLC in the presence of decoding complexity constraints. We consider transmission of short-blocklength codewords that are encoded with linear block encoders, transmitted over a binary-input AWGN channel, and finally decoded with order-statistics (OS) decoder. We investigate the optimal selection of a transmission rate and power pair, while satisfying the constraints. For this purpose, a multi-objective optimization problem (MOOP) is formulated. Based on the empirical model that accurately quantifies the trade-off between the performance of an OS decoder and its computational complexity, the MOOP is solved and the Pareto boundary is derived. 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.

To avoid brain damage in newborn infants, effective tools for prevention of excessive neonatal hyperbilirubinemia are needed. The objective of this study was to evaluate a new transcutaneous bilirubinometer (JAISY). For this purpose, 930 bilirubin measurements were performed in 141 newborn infants born near-term or at term (gestational age 35-41 weeks; postnatal age 1-6 days; 71 boys; including 29 infants with darker skin) and compared to those of a previously validated instrument (JM105). In each infant, the mean of three repeated measurements in the forehead was calculated for each instrument, followed by a similar measurement on the chest. The bilirubin values varied between 0 and 320 mu mol/l (0-18.8 mg/dl). There was a high degree of agreement with significant correlations between bilirubin values measured with the two devices on the forehead (Pearson's r = 0.94, p < 0.001) and the chest (r = 0.94, p < 0.001). The correlations remained after stratifying the data by gestational age, postnatal age and skin color. The coefficient of variation for repeated bilirubin measurements was 8.8% for JAISY and 8.0% for JM105 (p = 0.79). In conclusion, JAISY provides accurate and reproducible information on low to moderately high bilirubin levels in newborn infants born near-term or at term.

In this paper, 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 channel state information (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. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes.

In this article, we study the problem of latency and reliability trade-off in ultra-reliable low-latency communication (URLLC) in the presence of decoding complexity constraints. We consider linear block encoded codewords transmitted over a binary-input AWGN channel and decoded with order-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. Next, a consistent way to compute the aggregate latency for complexity constrained receivers is presented, where the latency due to decoding is also included. It is shown that, with strict latency requirements, decoding latency cannot be neglected in complexity constrained receivers. Next, based on the proposed model, several optimization problems, relevant to the design of URLLC systems, are introduced 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. Finally, it is also illustrated that the proposed model can closely describe the performance versus complexity trade-off for other candidate coding solutions for URLLC such as tail-biting convolutional codes, polar codes, and low-density parity-check codes.

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.

In this paper, we study the tradeoffs between complexity and reliability for decoding large linear block codes. We show that using artificial neural networks to predict the required order of an ordered statistics based decoder helps in reducing the average complexity and hence the latency of the decoder. We numerically validate the approach through Monte Carlo simulations.

The emergence of the Internet-of-Things (IoT), which will enable billions of devices to seamlessly connect with each other and to the Internet, aims to enhance the quality of daily life in diverse fields. Today, even though an abundance of IoT applications already exists, the growth of IoT is expected to accelerate in the foreseeable future. IoT applications are mainly divided into two categories: (1) consumer IoT and (2) industrial IoT (IIoT). The IIoT consists of interconnected sensors, machinery, and other “things” that are used in various fields of industrial applications. Throughout this chapter, the main focus is on wireless communication for IIoT applications and therefore the major challenges in designing a suitable wireless communication solution for IIoT applications are initially discussed. A comprehensive overview of the state-of-the-art wireless communication standards, which are suitable for IIoT applications, is presented and representative comparisons on some of the most common industrial wireless communication technologies including 5G, the next generation of the wireless technologies, are provided. Next, we focus on one of the most significant technologies for 5G systems, namely the ultra-reliable low-latency communication (URLLC), which is highly relevant for mission-critical IIoT applications. We list the challenges of URLLC and study the theoretical limits on the transmission of short packets. In these information theoretic works, latency is mostly computed as the total transmission time of a single packet. However, decoding a encoded packet is a computationally demanding operation and when we analyse complexity-constrained receivers, such as low complexity IIoT receivers, the time duration that is needed for decoding should also be taken into account in latency analysis. Finally, by including the decoding duration, we present the trade-offs in low-latency communication for receivers with computational complexity constraints.

Low-latency communication is one of the most important application scenarios in next-generation wireless networks. Often in communication-theoretic studies latency is defined as the time required for the transmission of a packet over a channel. However, with very stringent latency requirements and complexity constrained receivers, the time required for the decoding of the packet cannot be ignored and must be included in the total latency analysis through accurate modeling. In this paper, we first present a way to calculate decoding time using per bit complexity metric and introduce an empirical model that accurately describes the trade-off between the decoding complexity versus the performance of state-of-the-art codes. By considering various communication parameters, we show that including the decoding time in latency analyses has a significant effect on the optimum selection of parameters.

Squared-envelope receivers, also known as energy detectors, are, due to their simplified circuitry, low-cost and low-complexity receivers. Hence they are attractive implementation structures for future Internet-of-Things (IoT) applications. Even though there is considerable work on the wider research area of squared-envelope receivers, a comprehensive comparison and statistical characterization of training-assisted channel estimators for squared-envelope receivers appear to be absent from the literature. A detailed description of practical channel estimation schemes is necessary for the optimal training design of latency-constrained IoT applications. In this paper, various channel estimators are derived, their bias and variance are studied, and their performance is numerically compared against the Cramer-Rao lower bound.

Diffuse reflectance spectroscopy is a non-invasive spectroscopic technique for studying the optical properties of a biological tissue and hence can be used to detect chromophore concentration from the skin tissue. Here, a novel optical probe is presented to utilize diffuse reflectance spectroscopy. The proposed device contains an optical head that is easier to manufacture, more compact and more affordable than the existing fiber probes. This optical head consists of 19 fiber cables. The outer 12 fiber cables are used to expose the skin surface with a white light produced by a LED. And the reflected light emerging from the various layers of the tissue is collected by the inner 7 fiber cables, which is coupled to a spectrometer.