Indeed, it is possible to obtain algorithms that provide low energy losses (for example, Viterbi-type algorithms [15; 16]) but have high computational complexity. High complexity does not allow data processing in real time, which is often important, for example, when using portable receiving terminals of ground stations in DVB-S2/S2X satellite broadcasting systems. On the other hand, bit-by-bit algorithms are easy to implement, have low computational complexity and have significant energy losses. Moreover, energy losses increase with the increasing bit rate. In this regard, it is advisable to set the task of finding such processing algorithms that, with an increase in the data rate of FTN signals, allow an increase in energy losses of no more than specified for a fixed increase in computational complexity (for example, by more than 30%). Computational complexity can be estimated through the function \(O(x)\), which actually shows the dependence of the amount of calculations performed on the size of the input data/parameters of the algorithm. For example, in the case where the number of addition and multiplication operations (and, as a consequence, running time) depends linearly on the size \(n\) of the input data, the algorithmic complexity can be defined as \(O(n)\). When using an algorithm that is optimal according to the maximum likelihood criterion for receiving \"in general\" sequences of length \(N\) and signals with a volume of channel alphabet \(M\), in the general case it is necessary to make \(M^{N}\) comparisons of the observed implementation with all possible reference combinations of signals [17]. Then, the computational complexity of such an algorithm can be estimated as exponential: \(O(M^{N})\). In this way, it is possible to quantitatively compare different processing algorithms in terms of energy costs and computational complexity. In addition, it is possible to find the relationship between the increase in the number of addition and multiplication operations and the increase in the bit rate. Energy costs are the most important in cases of increasing the bit rate for various processing algorithms. The energy loss minimization for FTN signals under such conditions has been the topic of numerous research works [18; 19; 20; 21]. In these papers, the authors use coherent optimal and suboptimal processing algorithms: maximum likelihood sequence estimation (MLSE), the Viterbi algorithm, and so on. It has been shown that binary data can be transmitted at bit rates approximately 25% above the Nyquist limit without energy losses. The MLSE-based processing algorithm is implemented by enumerating all possible implementations of the FTN signal sequence. Solutions are obtained by finding the minimum Euclidean distance between the resulting implementation of the FTN signal sequence and all possible enumeration implementations. The computational complexity of the MLSE-based processing algorithm limits practical implementation because we need to use processing of the entire transmitted sequence of symbols, which is only possible when scale of the operation of the transmission system is not in real time. Nevertheless, it is possible to take the BER performance in this case as a reference for comparing other bit-by-bit processing algorithms with an assessment of energy losses relative to this algorithm. The struggle to reduce computational complexity for FTN signal processing has led to the emergence of a large class of bit-by-bit processing algorithms that have been discussed in scientific papers over the past 15 to 20 years. This emphasizes the relevance of solving the problem of demodulating FTN signals using simple methods under conditions of significant intersymbol interference. Among such algorithms, iterative processing algorithms with decision feedback can be distinguished [18; 19; 22; 23]. There are several modifications of these algorithms, both with a fixed observation bit interval and with an increased observation interval.