# Coexistence in Wireless Networks: Challenges and Opportunities
## 4 Spectrum Sharing and Coexistence
### Coexistence in Radar Networks  
Spectrum sharing between radar and communication systems has emerged as a critical research area. The growing prominence of radar technology, with applications ranging from autonomous vehicles to Internet of Things (IoT) devices, has intensified the need  
Figure 5: Coexistence of communication networks. for efficient spectrum utilization [58]. However, as the proliferation of radars continues, ensuring their ability to coexist and function without mutual interference is becoming increasingly vital. This challenge is referred to as coexistence in radar networks. Convolutional Neural Network (CNN)-based techniques for interference reduction and denoising in [59] produce promising performance outcomes for radar processing. Nevertheless, these models usually require more memory to operate because they have millions of parameters that are kept in hundreds of megabytes of memory [60; 61; 62]. Previously, studied examined quantization methods for CNN-based radar signal denoising and interference reduction. These studied investigated the potential of various CNN architectures and sizes for quantization by employing piecewise constant activation functions and quantized weights. This approach aims to reduce memory consumption during both model storage and inference. Figure 6 represents a deep learning-based interference mitigation model designed to process and reduce noise in communication signals. The input layer receives raw signals, which pass through multiple convolutional and feature extraction layers that identify interference patterns. These layers apply filtering, normalization, and activation functions to suppress interference while preserving the original signal. The final output layer produces a refined signal with minimized interference, enhancing communication reliability. In order to maximize the available bandwidth and accomplish both radar detection and communication, Ref. [63] offers a novel transmission approach. With a receiver system that enables channel estimation for radar without compromising the communication data rate, the technique combines orthogonal frequency-division multiplexing (OFDM) for communication and frequency-modulated continuous wave (FMCW) for radar. The results demonstrate that the system provides good sensing accuracy under low SNR circumstances and communication performance that is only slightly lower than a perfect CSI scenario. More specifically, the uplink of a large MIMO communication system coexisting with a radar system within the same frequency band was investigated in [64]. Radar-generated clutter at the MIMO receiver is considered in the system's model. Several linear receiver architectures, ranging from simple channel-matched beamforming to advanced zero-forcing and linear minimum mean square error techniques, have been proposed to mitigate the effects of channel congestion. These studies demonstrate that clutter significantly impacts cellular communication system performance and highlight the critical role of large-scale antenna arrays at the base station in enhancing data detection and system robustness. Figure 6: CNN architecture for denoising. A hybrid radar communication approach is depicted in [65], inserting data into chirp sub-carriers using Fractional Fourier Transform (FrFT) multiplexing with varying time-frequency rates. The goal of the optimization techniques are to improve the Bit Error Rate (BER) and bandwidth occupancy. When compared to a conventional Linear Frequency Modulated (LFM) pulse of comparable duration and bandwidth, the FrFT waveform preserves the radar characteristics while providing better performance, such as a lower BER and strong resistance to channel distortions. A solution for coexisting downlink communication and MIMO radar systems is provided in [66]. By employing a constructive MUI and exchanging multi-user interference power for reduced transmit power, the method strikes a compromise between power efficiency and performance.