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.
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):
Continue reading Why is MIMO Fading Capacity Higher than AWGN Capacity
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.
Continue reading MIMO, SIMO and MISO Capacity in AWGN and Fading Environment
Direction of Arrival (DOA) estimation is a fundamental problem in communications and signal processing with application in cellular communications, radar, sonar etc. It has become increasingly important in recent times as 5G communications uses DOA to spatially separate the users resulting in higher capacity and throughput. Direction of Arrival estimation can be thought of as the converse of beamforming. As you might recall from the discussion in previous posts, in beamforming you use the steering vector to receive a signal from a particular direction, rejecting the signals from other directions. In DOA estimation you scan the entire angular domain to find the required signal or signals and estimate their angles of arrival and possibly the ranges as well.
Continue reading Fundamentals of Direction of Arrival Estimation
The Electromagnetic Radiation from an antenna, particularly dipole antenna, has been studied in great detail. The mathematical framework proposed by Maxwell has stood the test of time and theoretical concepts have been verified through physical measurements. But the behavior of Electromagnetic (EM) waves close to the radiating antenna is not that well understood. This region that extends to about a wavelength from the antenna is called Near Field, as opposed to Far Field, which extends further out. The Near Field is further divided into Reactive Near Field and Radiative Near Field.
Continue reading Near Field of an Antenna
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
Continue reading Reconfigurable Intelligent Surfaces Explained
In a previous post we calculated the Bit Error Rate (BER) of a Massive MIMO system using two different channel models namely deterministic and probabilistic. The deterministic channel model is derived from the geometry of the array (ULA in this case) and the distribution of users in the cell. Whereas probabilistic channel model assumes that the channel is flat fading and can be modeled, between each transmit receive pair, as a complex, circularly symmetric, Gaussian random variable with mean of zero and variance of 0.5 per dimension.
Continue reading Massive MIMO and Antenna Correlation