Wireless/SDR

Converting Spatial Diversity into Time and Frequency Diversity

We have seen before that diversity implies using two or more statistically independent replicas for the transfer of the same information. This improves the reliability of the received message because nothing can recover the data signal in a deep fade except getting more copies of the same message. Spatial diversity has also been discussed in the context of multiple antenna systems. In this article, we describe how this diversity arising from the presence of multiple antennas can be converted into time or frequency diversity. Delay diversity converts spatial diversity into frequency diversity Phase-roll diversity converts spatial diversity into time diversity

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Time and frequency response of a lowpass FIR filter designed with Parks-McClellan algorithm for N=33
DSP

Finite Impulse Response (FIR) Filters

We learned in the concept of frequency that most signals of practical interest can be considered as a sum of complex sinusoids oscillating at different frequencies. The amplitudes and phases of these sinusoids shape the frequency contents of that signal and are drawn through magnitude response and phase response, respectively. In DSP, a regular goal is to modify these frequency contents of an input signal to obtain a desired representation at the output. This operation is called filtering and it is the most fundamental function in the whole field of DSP. It is possible to design a system, or filter,

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Analog beamforming
Wireless/SDR

What is the Difference between Analog, Digital and Hybrid Beamforming?

Beamforming is one of the most practical solutions to overcome higher path loss and atmospheric attenuation in mmWave bands. How it is implemented is a matter of great interest to RF industry due to the conflicting requirements of efficiency and flexibility. In a tradeoff between cost, size and complexity, analog beamforming is combined with digital beamforming to give rise to a hybrid solution, an architecture of choice in current 5G mmWave systems. Nevertheless, digital beamforming is inevitably the direction of future and it is only a matter of time before it will be used in 5G networks in high bands

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DFT-Precoded OFDM block diagram
DSP / Wireless/SDR

DFT-Precoded OFDM

One of the drawbacks of an OFDM waveform is its high Peak to Average Power Ratio (PAPR). This high PAPR arises from the fact that a set of $N$ QAM symbols are taken into time domain through an inverse Fourier Transform (iFFT) operation that basically generates a combination of complex sinusoids scaled by those symbols. Due to the variations between the symbol values and the sinusoids with different frequencies, the output waveform can have a large variance in amplitudes. This reduces the power amplifier efficiency that results in faster battery drainage in a mobile terminal. The effect on base station

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A trellis for implementing Viterbi algorithm
Wireless/SDR

Maximum Likelihood Sequence Estimation (MLSE Equalizer)

In the discussion on a wireless channel, we saw that an increased amount of Inter-Symbol Interference (ISI) occurs for high data-rate wireless systems that impacts the system performance to a significant extent. The performance of a linear equalizer suffers from spectral nulls. A Decision Feedback Equalizer (DFE) recovers much of the performance losses due to ISI but it is susceptible to error propagation. In 1972, David Forney published a paper titled "Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference" in which he proposed the idea of sequence estimation. For this purpose, there was an implicit assumption

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