Category Archives: Simulation

Simulation using MATLAB, Octave and Python.

Modeling Phase and Frequency Synchronization Error

Carrier phase or frequency synchronization is a common problem in wireless communication systems. These two problems are interrelated as instantaneous frequency is just the rate of change of phase. The problem of carrier frequency offset might appear due to one of two reasons. Either the oscillators at the transmitter and receiver are not aligned in the frequency domain or there is a Doppler shift introduced by the channel (remember that a moving object in the wireless environment introduces a Doppler shift). In the case of the former the frequency misalignment is given in parts per million (ppm). A typical value for commercially available oscillators is ±20 ppm. Assuming that there is maximum frequency error at both the transmitter and receiver the error increases to ±40 ppm. At 1GHz this translates to 40*1,000,000,000/1,000,000 = 40kHz.

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Index Modulation Explained

Wireless researchers are continuously exploring ways to increase the spectral efficiency (bits/sec/Hz) and energy efficiency (bits/Joule) of wireless communication systems [1]. Spectral efficiency can generally be improved by using larger constellations or by using multiple antennas at the transmitter and receiver, better known as MIMO. But increasing energy efficiency is not that straightforward. Let’s consider this in bit more detail.

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Reconfigurable Intelligent Surfaces Explained

Wireless channel is inherently unpredictable and this results in loss of information as it travels from the transmitter to the receiver. The main reason for this is that multiple copies of the wireless signal arrive at the receiver which sometimes add constructively and at other times destructively, causing deep fades. The deciding factor between signal copies (think of them as echoes) adding constructively or destructively is the relative phase. If the phases are aligned the signals add up but if the phases are not aligned, we get a fade (fades can be as deep as 60-80dB). Wireless engineers over the years have worked around this problem by using multiple antennas also called antenna arrays.

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MSK Demodulation Using a Discriminator

It is widely believed that performance of non-coherent receivers is much worse than performance of coherent receivers in terms of Bit Error Rate (BER). Although this is true to some extent but as we show in this post the difference in performance is not that much in case of Minimum Shift Keying (MSK). In fact, there is only a difference of about one dB in an AWGN environment at high Signal to Noise Ratios (SNR). The difference is somewhat larger in flat fading environment but given the simplicity of implementation of a non-coherent receiver the trade-off might be worth it.
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Orthogonal Minimum Shift Keying (OMSK)

Some Background

Before we delve deep into Minimum Shift Keying (MSK) and its performance in presence of co-channel interference the reader is advised to look at the following posts.

Post 1 – MSK BER performance in AWGN and flat fading environment when viewed as extension of BPSK

Post 2 – MSK Power Spectral Density and its BER performance in AWGN when viewed as a CPM

Post 3 – MSK BER Performance in AWGN and flat fading environment when viewed as a CPM

Co-channel interference is a phenomenon widely encountered in wireless communication systems and the main reason for that is frequency reuse, which allows the same frequency band to be used over and over again in geographically non-contiguous areas. GSM and other wireless communication systems, using MSK modulation, suffer from the same problem. This has been widely studied in the literature and interference rejection techniques have been proposed. The worst case is one where the power of both the signals (wanted signal and interference) is almost the same and there is no frequency or phase offset. 
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MSK Bit Error Rate in Rayleigh Fading

I - In the previous two posts we discussed MSK performance in an AWGN channel, first presenting the MATLAB/OCTAVE Code for one sample per symbol case [Post 1], and then extending it to the more general case of multiple samples per symbol [Post 2]. This helps us visualize the underlying beauty of Continuous Phase Modulation (CPM) which reduces out of band energy and consequently lowers Adjacent Channel Interference (ACI). We also briefly touched upon the case of MSK in Rayleigh fading, but did not go into the details. So here we take a deeper dive.
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