<<<All Things Wireless>>>
When a wireless signal travels from a transmitter (Tx) to a receiver (Rx) it undergoes some changes. In simple terms the signal s(t) is scaled by a factor h(t) and noise n(t) is added at the receiver.
Read more60GHz Millimeter Wave Band is a band spread between 57GHz and 64GHz. This unlicensed band was first released in the US in 2001 but with limited allowance for transmit power (EIRP of 40dBm). Later on, in 2013, this limit was increased (EIRP of 82dBm) to allow for greater transmit power and larger range.
Read moreWe calculate the degradation in Bit Error Rate (BER) performance due to antenna correlation at the Mobile Station and the Base Station. Exponential Correlation Model is used in the simulation.
Read moreThe mmWave Channel It is well known that wireless signals at millimeter wave frequencies (mmWave) suffer from high path loss, which limits their range. In particular there are higher diffraction and penetration losses which makes reflected and scattered signals to be all the more important. Typical penetration losses for building materials vary from a few dBs to more than 40 dBs [1]. There is also absorption by the atmosphere which increases with frequency. But there are also some favorable bands where atmospheric losses are low (<1dB/km).
Read moreDiscussion of Multicarrier Beamforming particularly in the context of Massive MIMO and Millimeterwave
Read moreArray Factor and Element Factor In the previous post we discussed the case of a Square Array which is a special case of a Rectangular Array. The code we shared can handle both the cases as well as Uniform Linear Array. We did briefly talk about the response of an element vs the response of an array, but we did not put forward the mathematical relationship. So here it is: Response of an Array = Array Factor x Element Factor In this post as well as previous posts we have assumed the element response to be isotropic (or at least […]
Read moreBackground In the previous few posts we discussed the fundamentals of Uniform Linear Arrays (ULAs), Beamforming, Multiuser Detection and Massive MIMO ([1], [2], [3], [4]). Now we turn our attention to more complicated array structures such as rectangular, triangular and circular. We still assume each element of the array to have an isotropic or omni-directional (in the plane of the array) radiation pattern. The mathematical models for more complicated radiation patterns are an extension of the what is developed here. Square and Rectangular Arrays In this post we consider a square array which is a special case of rectangular array. […]
Read moreBackground Just like different frequency bands and time slots can be used to multiplex users, spatial domain can also be exploited to achieve the same result. It is well known that if there are 4 transmit antennas and 4 receive antennas then four simultaneous data streams can be transmitted over the air. This can be scaled up to 8 x 8 or in the extreme case to 128 x 128. When the number of transmit or receive antennas is greater than 100 we typically call it a Massive MIMO scenario and we need specialized signal processing techniques to handle this […]
Read moreIn the previous two posts we discussed the fundamentals of array processing particularly the concept of beamforming (please check out array processing Part-1 and Part-2). Now we build upon these concepts to introduce some linear estimation techniques that are used in array processing. These are particularly suited to a situation where multiple users are spatially distributed in a cell and they need to be separated based upon their angles of arrival. But first let us introduce the linear model; I am sure you have seen this before. x=Hs+w Here, s is the vector of symbols transmitted by M users, H […]
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