Interference Avoiding Among QAM-MIMO Signals Transceiver Performance Based Bands Sharing

5G and other wireless technologies increase data transfer rates in constrained frequency ranges, however problems with broadband proliferation and ineffective resource distribution result in underutilized band and difficulties for cellular network operators. To solve these issues, more research is required on band sharing between public and private networks, including radars [1-4].

While band coexistence presents a viable way to use various frequency bands, 5G and radar equipment can cause interference. One of the main priorities should be controlling overlapping electromagnetic waves, especially those from radars. Band sharing does have certain disadvantages, too, like the requirement to shield incumbents from cellular system interference in common frequency regions. Furthermore, communication systems near radar systems cannot function properly due to the tremendous transmit power of radars overwhelming receiver amplifiers [5-7].

Full-duplex communication systems allow simultaneous data transmission and reception in the time and frequency domains, which maximizes the utilization of available band. With full-duplex technology, as opposed to passive band sharing, government organizations can use available radar frequencies or stay out of exclusion zones while still protecting themselves from disturbance. By increasing band availability and enhancing service quality, this approach opens the door for later band sharing projects including government users and cutting-edge technologies [8-10].

There are a number of proactive approaches in the literature that deal with interference caused by band sharing. Utilizing the diversity of waveforms is the key concept in many of the MIMO radar techniques that have been developed. Two primary types of MIMO radars can be distinguished based on the array configurations that are employed. In order to capture the spatial diversity of the target's radar cross section, the first type uses widely separated transmit/receive antennas. The second form of MIMO radar focuses on cohering a beam towards a certain direction in space by using arrays of closely spaced transmit/receive antennas. The relevant current research is discussed in the following. On the perspective of MIMO joint radar-communication systems, some of related works are described below. The authors investigated orthogonal frequency division multiplexing, orthogonal chirp division multiplexing, phase-modulated continuous wave, and sequence of chirps for a hybrid radar-communication system in 2021 [11]. The study reveals phase-modulated continuous-wave and sequence chirp modulation schemes are suitable for low data rate applications, including front-end overlap in integrated radar-communication systems, and necessitate multiplexing to prevent dynamic range decline. A thorough analysis of cooperative communication and radar systems was carried out [12]. The text is divided into three sections: communication, radar, and integrated communication and radar, emphasizing the need to consider millimeter wave and frequency-hopping signals for high data rates. In 2023, Nguyen et al. [13] have studied an effective joint radar-communications design that incorporates a MIMO-subcarrier allocation method. This method reduces the beam-forming's degrees of freedom and significantly degrades communication performance, particularly when there are stringent radar recognition limitations. In order to solve this, communications are given their own dedicated subcarriers, which remove interference from the radar function while maintaining strict design limitations to guarantee the radar's recognition accuracy. Current year, by combining radar and communications systems, Ozkaptan et al. [14] have proposed the mmWave to address the transmission band between vehicles and everything. Using the same waveform and technology for both operations, this approach proved to be a comprehensive way to use these bands. An OFDM-MIMO signal operating in the 24 GHz mmWave range was being tested. Despite being built on the MUSIC algorithm, the suggested system resulted in a significant level of computing complexity. Because of the enhanced radar processing capabilities made possible by the completely digital MIMO design, the findings have improved. On the other hand, the viewpoint of beam-forming system has been processed through radar-communication system too. A well-optimized time and band allocation technique for coexistence networks of distributed radar–communication is put out by Zhang et al. [15]. Subject to limitations on dwell time and band utilization, the system is built to minimize the sum of weighted position Bayesian Cramér-Rao lower bounds while meeting communication downlink needs. Simulations show that in terms of tracking performance, the suggested strategy performs better than two baseline allocation strategies. A performance analysis of the trade-off between MU-MIMO and MIMO communication systems is provided by Chen et al. [16]. The study utilized weighted mean square error and sequential convex approximation techniques to determine the optimal border region for communication and radar foci, and expanded the study to create a simpler level of beam-forming for channel selection and zero-forcing scenarios. He et al. [17] propose a modal using the penalty dual decomposition technique to address radar transmitting power issues in both low-power and high-power settings. They also create a beam-forming system using block coordinate descent to prevent interference and reduce complexity using a non-convex objective function.

In this paper, a performance model for preventing interference between 5G communications systems and radar is suggested. Both 5G and radar communication systems use many antennas on both the transmitter and the receiver in accordance with the MIMO principle using QAM signal. The proposed model will prohibit the interference that may occur through a channel with Rayleigh fading kid between communications system and radar-based MIMO-5G technique. Additionally, it is also damped the weakness in the MIMO-radar performance. The suggested model is also used to MIMO sub-arrays in order to investigate the interference that occurs between MIMO radar and regular QAM signals. The contributions of the proposed model can be summarized as follows:

• The proposed model eliminates the side-lobe carefully then the interference will be decreased.
• The band sharing of focusing radar is efficient since the channel among the 5G communication systems has a nullified-space.
• The coexistence between communications and radar systems has been enhanced due to the overlapping of MIMO radar.

The rest of this paper is organized as follows. Section II describes the fundamentals of sharing of bands through the MIMO. Section III introduces the proposed avoiding-overlapping MIMO radar system. Section IV discusses and analyses the results of simulation running and offers details with benchmarks. Last Section V determines the article with suggestion of future works.

The unique needs of both tasks should be the main-focus of system design and optimization in order to create a reasonable compromise between radar and communication performance. The joint optimal waveform design presents the main challenge. Phased array, fully-digital MIMO, and hybrid array architectures are the three types of hardware for such systems. They employ analog, digital, and hybrid analog-digital beam-forming, respectively, as their respective beam-forming methodologies.

• Analog beam-forming: Phase shifters are used to feed a single signal to every antenna, directing it in a certain direction. It is straightforward, economical, power-efficient; however, it can only produce a single beam, and in wideband transmissions, it exhibits beam squint.
• Digital beam-forming: Similar to MIMO systems, each antenna has its own RF chain and uses spatial pre-coding to create signals in the digital baseband. Although this method is more complicated, costly, and power-intensive, it supports multiple beams and wideband operation.
• Hybrid beam-forming: Creates sub-arrays within of a larger array and uses analog beam-forming within each sub-array. This technique combines elements of both analog and digital beam-forming. It lessens the drawbacks of both pure analog and digital methods by providing a performance balance.

It is possible to construct performance criteria for communication and radar systems separately or as a weighted composite function. Mutual information is the greatest amount of data that can be transferred between a source and a receiver in a communication system. For radars, it measures the quantity of information gained from the received signal about the environment. Optimizing the radar-communication waveform using a benchmark signal with a desirable beam-pattern—typically a well-known radar waveform with low side-lobe levels and strong correlation—is another method of design. In order to ensure that the waveform maintains a beam-pattern identical to the radar's while meeting communication needs this reduces the impact of communication data and channel randomness. Minimizing the mean-square estimation error for important parameters like range, angle, and velocity is the key to accurate finding in radar systems. The Cramér-Rao Lower Bound is a crucial performance metric that establishes the lower bound for mean-square estimation error. To maximize identifying accuracy, Cramér-Rao Lower Bound must be taken into account in the collaborative design of communication-radar waveforms since it is impacted by transmitted waveforms.

To introduce the proposed system with details, this section is divided into two following sub-section as shown below:

3.1 MIMO-radar avoiding-overlapped formulation

A novel antenna design based-MIMO is presented in current sub-section, it is also known as overlap-MIMO, which allows beam-forming for both transmit and receive arrays by splitting every array into many overlapped sub-arrays. A fundamental idea is fragmenting the transmitted arrays into many sub-arrays, i.e., K, allowing for overlap [24].

At the kth sub-array's output, the signal complex envelope is represented as

$\begin{gathered}s_k=\sqrt{\frac{M_T}{K}} \phi_k(t) \odot w_k, \quad k=1, \ldots, K \\ \text { and } 1 \leq K \leq M_T\end{gathered}$    (9)

where, the vectors of M_m×1 includes $\phi_k$ and $w_k$. The last vector contains both real and imaginary components with a unit-norm that contains M_m beam-forming weights. M_m orthogonal waveforms make up the waveform vector that is the former. It is noteworthy that M_m denotes the quantity of antenna elements present in every sub-array. M_m is defined as M_T−K+1.

The transmitted signal, $\phi(t)_k^m$, can be formulated as follows for every orthogonal waveform,

$\phi_k^m=Q(t) e^{j 2 \pi\left(m k / T_0\right) t}$    (10)

where, the pulse shape is represented by Q(t) for duration T₀, m = 1, …, M_m and k = 1, …, K [25].

When a target is placed in the far field at angle θ, the signal that is reflected off it is described as:

$r(t, \theta)=\sqrt{\frac{M_T}{K}} \beta(\theta) \sum_{k=1}^K \sum_{m=1}^{M_m} w_k^m \phi_k^m(t) d_k^m(\theta)$     (11)

where, $\beta(\theta)$ is the coefficient of reflection, and $d_k^m(\theta)$ is the vector of waveform diversity that is defined as $e^{-j \tau_k^m(\theta)}$. In addition, the necessary time, $\tau_k^m(\theta)$, for the wave to travel from the first component to the next component.

It is possible to express the complex vector that was received from the array observation as

$y_{\text {Radar}}(t)=r\left(t, \theta_s\right) b\left(\theta_s\right)+\sum_i^{\varepsilon} r\left(t, \theta_i\right) b\left(\theta_i\right)+n_{\text {Ray }}(t)$     (12)

where, ε is the interfering signals number, $b(\theta)$ is the vector of receive steering with size M_R×1 associated with angle θ, and n_Ray(t) is a Rayleigh-fading channel noise.

A vector of essential data may generate as [1×M_RKM_m]^T, by filtering with matched-filter y_Radar(t) for every waveform that is noted with $\left\{\phi_k^m\right\}_{m=1, k=1}^{M_m, K}$.

$y_v=\sqrt{\frac{M_T}{K}} \beta_s u\left(\theta_s\right)+\sum_i^{\varepsilon} \sqrt{\frac{M_T}{K}} \beta_i u\left(\theta_i\right)+n_{\text {Ray }}(t)$    (13)

$u(\theta)=(c(\theta) \odot d(\theta)) \otimes b(\theta)$    (14)

Eq. (14) represents a vector of virtual steering with [1×M_RKM_m]^T, a middle vector $c=\left\{w_k^m a_k^m\right\}_{m=1, k=1}^{M_m, K}$ of size [1×M_mK]^T, and vector of waveforms diversity $d= \left\{e^{-j \tau_k^m(\theta)}\right\}_{m-1, k-1}^{M_m, K}$ of size M_mK×1 [26].

The beam-former weight vectors for the kth transmitting sub-array in non-adaptive beam-forming are given as.

$w_k=\frac{a_k\left(\theta_s\right)}{\left\|a_k\left(\theta_s\right)\right\|}=\frac{a_k\left(\theta_s\right)}{\sqrt{M_T-K+1}}, \quad k=1, \ldots, K$    (15)

Such vectors are given for the receiving sub-arrays as $w_d= \left(c\left(\theta_s\right) \odot d\left(\theta_s\right)\right) \otimes b\left(\theta_s\right)$.

Let Norm(θ) be the normalized overall beam-pattern.

$\operatorname{Norm}(\theta)=\frac{\left|w_d^H u(\theta)\right|^2}{\left|w_d^H u\left(\theta_s\right)\right|^2}=\frac{\left|u^H\left(\theta_s\right) u(\theta)\right|^2}{\left\|u\left(\theta_s\right)\right\|^4}$     (16)

$a_1^H a_1\left(\theta_s\right)=\cdots a_K^H a_K\left(\theta_s\right)$ is obtained specifically for the situation of a uniform linear array. For a uniform linear array with K overlapped transmit sub-arrays, the beam pattern of the overlapped-MIMO radar can be stated as follows using Eq. (16):

$\begin{gathered}\operatorname{Norm}_K(\theta)= \\ \frac{\left|a_K^H\left(\theta_s\right) a_K(\theta)\left[\left(d\left(\theta_s\right) \otimes b\left(\theta_s\right)\right)^H(d(\theta) \otimes b(\theta))\right]\right|^2}{\left\|a_K^H\left(\theta_s\right)\right\|^4\left\|d\left(\theta_s\right) \otimes b\left(\theta_s\right)\right\|^4}\end{gathered}$     (17)

3.2 Sharing of bands based on MIMO-radar system

This subsection details a radar projection algorithm-based band-sharing technique, utilizing a projection approach of null space [27], the approach projected the signal of overlap-MIMO radar on a communication interference channel's null space, requiring prior channel side information that can be obtained and shared with the radar system [28].

The suggested technique works like this: the radar first gathers H, the channel side data that connects the radar to the communication channels. The available number of null space that are for projection will be calculated, and it equals M_T−N_R. Next, it determines the projection of the channel matrix, P, then produces a fresh signal of radar, $\hat{x}_{\text {Radar }}$. In order to prevent radar interference, the overlapped-MIMO radar waveform projected onto H's null space can be written as follows: where H demonstrates the channel matrix.

$\hat{x}_{\text {Radar}}(t)=\boldsymbol{P} x_{\text {Radar}}(t)$    (18)

This paper presents an overlapped-MIMO antenna design and the null space projection band-sharing technique to allow coexistence of communication systems and radars. In the presence of Rayleigh fading channel noise, the system evaluates the MIMO concept based on 5G signals. The radar's transmit array in the overlapped-MIMO design is split up into many overlapping sub-arrays, each of which sends signals orthogonal to those of the other sub-arrays and to each other. Comparing this architecture to traditional MIMO radar, it improves side-lobe suppression, boosts diversity gain, and expands the effective transmit array size, all of which make it more compatible with communication systems. It is advised that this system be investigated further in the context of 6G, whether or not NOMA integration is present.