## 3 Related Work  
### TPC and Methods Other than SIC in Non-THz Communications  
TPC is a quite extensively researched study from the period of wireless sensor networks (WSN) and continues to the day [8; 9; 10]. It has been used for link quality estimation and optimization, and topology control [11; 12], localization and energy efficiency [13], in conjunction with the rate for vehicular networks [14], as well as 5G [15; 16]. In this section, we provide the reader with THz communication-related works. Also, there are numerous interference cancellation methods apart from SIC as well as other methods that are given below. In [17], an information-theoretic analysis of the simplest multi-hop network, the two-hop source-relay-destination system, leads to the development of a novel transmission technique known as structured self-interference cancellation (or \"structured cancellation\" for short). In this strategy, the source intentionally avoids transmitting at certain signal levels, while the relay designs its transmission to learn the residual self-interference channel. By observing the portion of its own transmitted signal that appears at the vacant signal levels left by the source, the relay can cancel the self-interference. The study demonstrates that, in certain complex scenarios, the structured cancellation method not only outperforms half-duplex systems but also surpasses full-duplex systems that rely on time-orthogonal training for estimating the residual self-interference channel. In [18], the paper explores a multi-source video multicast in Internet-connected wireless mesh networks (WMNs). The primary focus is on designing a shareable integrated multicast framework that enables video source multicasts to utilize common Internet shortcuts or WMN paths, minimizing the potential overhead of WMNs and excessive Internettraffic. Several algorithms are introduced, collectively forming a video multicast framework that manages a controlled number of shareable multicasts, subject to the availability of Internet resources. These algorithms include the resource-efficient source group algorithm, the efficient integrated architecture algorithm, and the interference-controlled multicast tree algorithm. In [19], the authors propose and analyze parallel low-rate transmissions alongside alternative rate transmissions to investigate the benefits of Multi-Rate Multi-Channel (MRMC) in enhancing the performance and coverage tradeoff, while operating within the constraints of limited channel resources. These new transmission schemes are then applied to optimize the WMN multicast experience. By integrating reliable interference-controlled connections, the authors design a novel MRMC multicast algorithm, LC-MRMC, which efficiently utilizes channel and rate resources, significantly expanding wireless multicast coverage with high throughput and low-delay performance. In [20], the study addresses the challenge of achieving a high Packet Delivery Ratio (PDR), energy efficiency, and cost-effective clustering and routing in WSNs. To reduce deployment costs, a heterogeneous setup is considered, featuring energy-constrained normal sensors for environmental monitoring, alongside high-energy, TPC-enabled multi-radio super nodes. These super nodes function as cluster heads and are responsible for collecting data from the normal sensors. By equipping super nodes with additional radios, static channel assignment is enabled, resulting in high PDR with minimal network overhead. This TPC-based multi-channel heterogeneous setup offers cost-effectiveness, high PDR, and energy efficiency. The problem is decoupled into two phases: configuring the super nodes and normal sensors, which are optimized using the Grey Wolf Optimization (GWO) algorithm. In [21], the authors propose an innovative approach in which TinyML models are seamlessly integrated into access points (APs) for adaptive channel optimization and transmission power control.