![]() Finally, some open challenges and future research topics are envisaged. Afterwards, the application of interference utilization precoding is discussed in multi-cluster scenario. We first briefly introduce the concept of constructive interference, and then we present two generic downlink interference-utilization optimizations, which utilizes the multi-device interference for enhancing system performance. In this review paper, we aim to review the emerging interference utilization precoding techniques. In recent years, a judicious interference utilization precoding has been developed, which is capable of exploiting multi-device interference as a beneficial element for improving device’s reception performance, thus reducing downlink communication latency. In Internet-of-Things, downlink multi-device interference has long been considered as a harmful element deteriorating system performance, and thus the principle of the classic interference-mitigation based precoding is to suppress the multi-device interference by exploiting the spatial orthogonality. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. In addition to enjoying the superiority of SLP in reducing SER or transmit power for MU-MISO systems, researchers have also devoted themselves to combining SLP with other techniques, such as simultaneous wireless information and power transfer (SWIPT), , cognitive radio (CR), , fasterthan-Nyquist (FTN) signaling, multi-cell scenario, physical layer security (PLS), , intelligent reflecting surfaces (IRS) -, integrated sensing and communication (ISAC), , to take the advantages of the temporaldomain flexibility and the CI. With the wellestablished model of SLP for MU-MISO systems, various practical hardware limitations have been taken into account, such as the constant envelope architecture with low peak-toaverage-power ratio (PAPR) constraint, , the lowresolution digital-to-analog converter (DAC), the hybrid analog-digital architecture, , and the single radio frequency (RF), RF-domain architecture, etc. The initial works, verified the superiority of this symbol-to-precoder scheme in significantly reducing the symbol-error-rate (SER) of multi-user multi-input singleoutput (MU-MISO) systems by exploiting MUI. ![]()
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