A 2x2 MIMO spatial multiplexing system

Linear Detection Algorithms in MIMO Systems

In the past 100 years, scientists have imagined new ways of boosting the capacity of wireless channels. Around the middle of 20th century, we began to truly understand the role of fundamental players in this equation, namely power and bandwidth. It was realized that the capacity of a wireless channel increases logarithmically with SNR and hence quickly approaches the region of diminishing returns. Nevertheless, with a few exceptions, almost all the research was exclusively focused on single antenna systems. It was only in mid 1990s that the power of using multiple antennas at both ends of the link was discovered.

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A quasi-static assumption implies that the channel stays the same for each block but varies from one block to the next

A Time-Varying Wireless Channel

Today we will discuss three strategies that are usually adopted for handling a wireless channel that is varying with time and hence acting differently on different data symbols. For a channel impulse response $c(t)$, number of multipath $N_{MP}$, channel gains $\gamma_i(t)$ and delays $\tau_i(t)$ for the $i$-th path, respectively, we can write \begin{equation*} c_B(t) = \sum _{i=0}^{N_{MP} -1} \gamma_i(t) \cdot \delta(t-\tau_i(t)) \end{equation*} i.e., channel gains $\gamma_i(t)$ and channel delays $\tau_i(t)$ are varying with time albeit at different rates. With the movement in the channel, the taps in a frequency selective channel are changing according to the rotation rates of path

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Interpreting Time Domain Derivative in Frequency Domain

Although this article explains the concepts in terms of mathematical constants e and j as well as integration, my book on SDR steers clear of the complex notation and integrals to describe the underlying concepts from the ground up to an advanced level. One of the properties of Fourier Transform is that the derivative of a signal in time domain gets translated to multiplication of the signal spectrum by $j2\pi f$ in frequency domain. This property is usually derived as follows. For a signal $s(t)$ with Fourier Transform $S(f)$ \begin{equation*} s(t) = \frac{1}{2\pi}\int \limits _{-\infty}^{+\infty} S(f) e^{j2\pi ft}df, \end{equation*} we

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Bandpass sampling

On Analog-to-Digital Converter (ADC), 6 dB SNR Gain per Bit, Oversampling and Undersampling

We have discussed before the sampling on time axis for analog to digital (A/D) conversion. An Analog to Digital Converter (ADC) produces the samples $x[n]$ of a continuous-time signal $x(t)$ at its input. Ideally, these samples are the exact values of the signal $x(t)$ at time instants $nT_s$ where $T_s=1/f_s$ is the sampling period. In practice, however, there are imperfections both on the y-axis and the x-axis. On y-axis, an ADC has a finite resolution depending on the number of bits used for quantization. On x-axis, there are issues of clock jitter that distort the samples produced. In this article,

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Diversity implies two or more independent replicas of the same information

Multiple Antenna Techniques

When computing approaches the physical limits of clocking speeds, we turn towards multi-core architectures. When communication approaches the physical limits of transmission speeds, we turn towards multi-antenna systems. What exactly are the benefits that led to scientists and engineers choosing multiple antennas as the foundation of 4G and 5G PHY layers? While having spatial diversity was the original incentive for adding antennas at the base stations, it was discovered in mid 1990s that multiple antennas at Tx and/or Rx sides open up other possibilities not foreseen in single antenna systems. Let us now describe three main techniques in this context.

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