A Primer on Ray-Tracing: Shooting and Bouncing Ray Method
Ray-tracing is a promising alternative for Radio Frequency Planning particularly in urban areas. There are two fundamental techniques used for ray-tracing namely Shooting and Bouncing Rays and Method of Images. In this paper, we focus on the former and present simulation results for an urban scenario in the city of Helsinki. We also give an insight into how the Shooting and Bouncing Ray method can be implemented using basic linear algebra techniques. We show that ray-tracing can be used to evaluate the performance improvement attained through electromagnetic reflectors. Finally, we close the discussion by outlining the existing challenges and the way forward.
Orthogonal Minimum Shift Keying: A New Perspective on Interference Rejection
Co-Channel Interference is a classical problem in cellular systems that has been studied extensively and several methods have been proposed to overcome it. These include interference rejection techniques as well as joint detection techniques. We have previously proposed a joint detection technique for MSK-type signals that works quite well in certain conditions. In this paper, we formally present what we call Orthogonal MSK and postulate that if two MSK signals have a 90-degree phase offset between them then both can be detected successfully increasing the spectral efficiency two-fold. This technique works well even if the two signals are near equal power and have the same carrier frequency.
Why is MIMO Capacity in a Fading Environment Higher than in an AWGN Environment
A wireless channel suffers from two fundamental impairments; fading and noise. While fading is multiplicative, noise is additive. It is well-known that higher the noise, lower is the signal to noise ratio and lower the capacity. However, fading can be helpful in increasing the capacity when using multiple transmit and receive antennas. In this paper, we give an intuitive explanation for this. Anybody with a background in linear algebra and matrices can understand this.
This textbook covers fundamental topics in Telecommunication including Channel Modeling, Modulation/Demodulation, Channel Coding/Decoding, Multicarrier, Capacity, Antenna Arrays, Diversity, and 4G/5G. It will also cover advanced topics such as High-Resolution Spectral Estimation, Reconfigurable Intelligent Surfaces, Index Modulation, Full-Duplex, and Millimeter Wave. This book will mainly target engineering students (both graduate and advanced undergraduate) who are new to the fields of Communication and Signal Processing and are struggling to understand the fundamental concepts. This book will help the students step by step by introducing the concepts first in their most basic form and then providing the code that the students can experiment with. It contains pedagogical elements such as chapter introductions, end-of-chapter questions and numerical problems, MATLAB/Octave/Python code, figures and tables, and a website (raymaps.com) for feedback and interaction. It will not only be helpful for undergraduate and graduate students but also for professional engineers and hobbyists.
Yasir Ahmed has more than 20 years of experience in various organizations in Pakistan, Europe, and the USA in both Engineering and Management roles. He worked as a Research Assistant in the Mobile and Portable Radio Group (MPRG) of Virginia Tech under the supervision of Dr. Jeff Reed and was one of the first researchers to propose Space Time Block Codes (STBCs) for eight transmit antennas. The collaboration with MPRG has continued over the years and has resulted in 12 research publications and a book on Wireless Communications. Yasir worked as GM SEED at Ignite National Technology Fund, a company involved in supporting the innovation and entrepreneurship ecosystem in the country. He previously worked for Qualcomm USA, leading the physical layer performance and conformance testing of GSM/UMTS modems, and for COMSATS Islamabad as an Assistant Professor, teaching various subjects in the Telecom and Networks area. He was part of the Ignite team that evaluated multi-billion-rupee NIC and DigiSkills programs and has also helped fund a number of startups that have gone on to become successful commercial ventures.
The Fourier Transform is often used in Communication and Signal Processing to find the spectral content of a time-domain signal. The most common example is that of a sinusoid in the time domain, resulting in a sharply peaked signal in the frequency domain, also known as a delta function. A rectangular pulse in the time domain has a more complicated frequency domain equivalent, a sinc function. A rectangular pulse may be thought of as a combination of many sinusoids, hence its frequency domain equivalent is not that straightforward.
In a previous post we have seen that MIMO fading capacity is much higher than AWGN capacity with multiple antennas. How is this possible? How can randomness added by a fading channel help us? In this post we try to find the reason for this. Let’s assume the following signal model for a Multi Input Multi Output antenna system.
x=Hs+w
Here s is the NT by 1 signal vector, w is the NR by 1 noise vector and H is the NR by NT channel matrix. The received signal vector is represented by x which has dimensions of NR by 1. In expanded form this can be written as (assuming NT =4 and NR =4):
In a previous post we had discussed MIMO capacity in a fading environment and compared it to AWGN capacity. It sometimes feels unintuitive that fading capacity can be higher than AWGN capacity. If a signal is continuously fluctuating how is it possible that we are able to have reliable communication. But this is the remarkable feature of MIMO systems that they are able to achieve blazing speeds over an unreliable channel, at least theoretically. It has been shown mathematically that an NxN MIMO channel is equivalent to N SISO channels in parallel.
Recently I came across a post from T-Mobile in which they claim to have achieved a download speed of 5.6 Gbps over a 100 MHz channel resulting in a Spectral Efficiency of more than 50 bps/Hz. This was achieved in an MU-MIMO configuration with eight connected devices having an aggregate of 16 parallel streams i.e. two parallel streams per device. The channel used for this experiment was the mid-band frequency of 2.5 GHz.
