n transceiver complexity, which will be discussed in Section 6.5. Based on our findings in Sections 6.4 and 6.5, we note that window functions (at the transmitter and receiver) that may be effective in converting the channel to a sparse one in the delay-Doppler space require knowledge of channel variations on a given data packet at the transmitter, which obviously is unknown at the time of transmission. Hence, it is hard to say whether any effective window in this sense could be applied at the transmitter. On this basis, we will ignore the windowing step of the OTFS at the transmitter side. At the receiver side, on the other hand, an iterative channel estimation and equalization may allow the use of an effective window that leads to a sparse channel, e.g. [6, 7]. This is an interesting topic that falls out of the scope of this chapter and may be left for future studies. Thus, in Section 6.5, we include time-frequency signal windowing at the receiver side, but limit our discussion to the case where this is a separable window along the time and frequency dimensions and emphasize on channel-independent window functions with some reasonable impact. From system implementation point of view, computational complexity is a very important aspect that needs to be analyzed. Therefore, Section 6.6 analyzes and compares the computational load of the simplified modem structure that is presented in Section 6.5 with that of the conventional OTFS implementation and also OFDM. Finally, Section 6.7 covers recent results on OTFS from the literature while discussing some potential research directions in this area. Notations Throughout this chapter, matrices, vectors, and scalar quantities are denoted by boldface uppercase, boldface lowercase, and normal letters, respectively. [A]mn" a(n), and A-Superscript(1) repre- sent the mnth element, the column n, and the inverse of A, respectively. {A} vectorizes A by stacking its columns on the top of one another in a column vector. I'm is the identity matrix of size m. The superscripts (.)T, 