Learning Outcomes
After completing this module, the students will have a basic knowledge of wireless communications systems and they will be able to master some fundamental mathematical methods that are widely used in the analysis and optimization of modern wireless communications systems. In particular, the students will learn how to model the wireless channel and how to exploit the spatial diversity using multiple antenna systems. Further, the lectures intend to convey a basic understanding of modulation and multiple access techniques such as CDMA and OFDMA. Finally, the lectures will provide initial insights into the design of wireless communication networks in the context of the evolving fields of Internet-of-Things, Industry 4.0, intelligent transportation, and smart grids. Regarding the mathematical methods for the analysis and optimization of wireless communications systems, the students will learn how to use mathematical methods when designing modern wireless communications networks. In doing so the lectures will combine the mathematical precision with practical examples. As a result, the acquired knowledge will enable the students to better understand complex interdependencies in such networks, which is essential for efficient design and operation of wireless networks.
Content
The learning content includes:
- A brief overview of typical wireless communications scenarios, the main challenges and differences when compared with wired communications
- Wireless channel as a time-varying linear system (time-varying impulse response), large-scale and small-scale fading, multi-path fading, existing approaches to modeling of wireless channels
- Basic principles of stochastic modeling for wireless channels, Rayleigh and Rician channels, log-normal shadowing
- Time-frequency correlation functions, wide-sense stationary uncorrelated scattering model, Doppler spread and coherence time, delay spread and coherence bandwidth, flat versus frequency-selective fading
- Performance measures used in wireless communications: signal-to-noise ratio, rate, ergodic capacity, outage capacity, delay-limited capacity
- Definitions of time, frequency and spatial diversity, other notions of diversity
- Some basic diversity techniques including repetition coding, maximal ratio combiner (RAKE receiver), receive antenna diversity (SIMO), transmit antenna diversity (MISO), the impact of channel state information
- Principles of spread-spectrum techniques and orthogonal frequency division multiplexing (OFDM)
- Basic multiaccess techniques including TDMA, FDMA, DS-CDMA and OFDMA
- Random access techniques including traditional ALOHA/slotted ALOHA and contemporary solutions based on coded random access
- Enabling technologies for massive connectivity and efficient spectrum utilization, including massive MIMO systems and cloud-radio access networks (C-RANs)
- Tradeoffs between throughput, reliability and latency in emerging communication scenarios including massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC)
- Mathematical methods that are used to solve many real-world problems in modern wireless communications systems/networks. As concrete applications that are in the focus of the lectures, we cite interference reduction in spread spectrum and MIMO systems, adaptive beamforming, PAPR reduction in OFDM systems. In particular, a special attention is attached to the following topics: basic principles of (functional) analysis that are relevant in the design of modern communications systems, fundamentals of matrix analysis, fundamentals of (convex) optimization theory, Bayesian inference, graphical models, projection methods, principles of convex relaxation, algorithm design, convergence properties.
Description of Teaching and Learning Methods
The module consists of conventional frontal teaching in class, developing theoretical and mathematical concepts, exercises developed in class, in order to develop problem-solving skills and reinforce comprehension of the theory, and homework exercises in order to develop independent and autonomous thinking skills in the students.
