The Internet of Things (IoT) is the fourth technological revolution in the global information industry after computers, the Internet, and mobile communication networks. It combines radio-frequency identification devices, infrared sensors, global positioning systems, and various other technologies. Information sensing equipment is connected via the Internet, thus forming a vast network. When these physical devices are connected to the Internet, the user terminal can be extended and expanded to exchange information, communicate with anything, and carry out identification, positioning, tracking, monitoring, and triggering of corresponding events on each device in the network. In real life, the IoT has a wide range of applications, covering many fields, such as smart homes, smart logistics, fine agriculture and animal husbandry, national defense, and military. One of the most significant factors in wireless channels is interference, which degrades the system performance. Although the existing QR decomposition-based signal detection method is an emerging topic because of its low complexity, it does not solve the problem of poor detection performance. Therefore, this study proposes a maximum-likelihood-based QR decomposition algorithm. The main idea is to estimate the initial level of detection using the maximum likelihood principle, and then the other layer is detected using a reliable decision. The optimal candidate is selected from the feedback by deploying the candidate points in an unreliable scenario. Simulation results show that the proposed algorithm effectively reduces the interference and propagation error compared with the algorithms reported in the literature.
The sixth generation (6G) aims to provide a 1000× factor of transmission capacity increase, at least 100 billion Internet-of-Things (IoT) device connections, a transmission rate of up to 10 Gbit/s, and an ultra-low latency user experience in the range of ms [
In IoT communications, anti-interference of the wireless channel is still affected by various factors [
Aiming at solving the problem of poor detection of the conventional QR method in multi-antenna systems, this study proposes a QR decomposition scheme based on ML criteria and a candidate mechanism. This algorithm greatly improves the performance while featuring low complexity.
A multi-antenna system is shown in
The received signal can be expressed as follows:
The core idea of the QR decomposition algorithm is to decompose the channel matrix to obtain an upper triangular matrix and an orthogonal matrix, and then use the correlation properties of the matrix to detect the received signal. Compared with the MMSE and ZF algorithms, the QR decomposition algorithm avoids the calculation of the channel matrix inversion and effectively reduces the computational complexity of the detection algorithm [
When the number of antennas at the receiving end is not less than the number of antennas at the transmitting end, QR decomposition is performed on channel
The QR decomposition algorithm detects layer by layer, and inevitably there is error propagation between two adjacent detection layers, leading to system performance degradation. If there is an error in the signal estimation of the first detection layer, it will affect the signal estimation of all subsequent layers. Therefore, the order of detection is critical for the QR algorithm, and the performance of the entire system largely depends on the first detection layer [
For QR decomposition detection algorithms, the correctness of the initial detection layer directly affects the signals of other detection layers. Ensuring the correctness of the first detection layer can effectively reduce the error propagation. The framework of the proposed ML-QR algorithm implementation is shown in
The ML criterion is applied to the initial detection layer of the QR detection algorithm, that is, the modulation constellation points are sequentially used as the estimated model of the first layer, and then the QR detection algorithm is performed on the remaining layers. The optimal constellation point among the constellation points is selected for feedback as the initial detection layer estimation signal. After the initial layer signal is determined, the reliability of the soft estimation of the remaining layers is judged, and if the judgment is reliable, the candidate point is selected for optimal feedback [
The reliability decision scheme is shown in
First, the modulation constellation points,
If
If the constellation point is represented by
The soft decision
The process of obtaining optimal feedback candidate points is described next.
To determine the best candidate point, we define the selection vector The previously detected symbols The The symbols from the
Thus, the following expression can be obtained:
1: for |
2: |
3: for |
4: |
5: end for |
6: end for |
7: |
8: |
9: for |
10: |
11: if unreliable in |
12: for |
13: for |
14: |
15: end for |
16: |
17: end for |
18: |
19: |
20: Else |
21: |
22: end if |
23: end |
The first inspection layer with constellation points is in turn replaced, and a QR decomposition algorithm is in turn performed. The complexity of QR decomposition is mainly based on the decomposition of
The soft judgment reliability judgment is denoted by
Let us assume that under flat Rayleigh fading channel in a multi-antenna system, the data sent by the transmitting antennas are mutually independent, the data frame length is 20,000, the unwoven system and QPSK modulation mode are used, and the algorithm performance is measured by the bit error rate [
In
Overall, the proposed detection algorithm can significantly improve the system performance with a certain degree of complexity. Simultaneously, it can effectively control the performance and complexity of the system through the threshold and the number of candidate points, thereby achieving a balanced compromise between the performance and complexity of the detection algorithm. The ideological response has a positive effect on reducing the cost of IoT communication and improving its reliability.
An effective error detection algorithm can greatly improve the communication performance of the IoT. The proposed algorithm can accurately estimate the initial detection layer signal through the principle of the ML criterion algorithm. Specifically, the proposed system model uses the shadow area constraint to enhance the interference cancelation and effectively reduces the impact of error propagation. In addition, it can ensure that the complexity caused by unreliability is reduced, and the candidate mechanism can be used to suppress the propagation of false judgments in the entire detection process effectively and improve the system performance. According to MATLAB simulations of the system, it is shown that the algorithm can greatly improve the system performance and effectively save the signal-to-noise ratio. In addition, the proposed algorithm can control the computational complexity and improve the system performance according to the threshold and number of candidate points, thereby achieving a good balance between detection performance and complexity. A potential future study could consider the mean square error and throughput analysis.
The author extends his appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number (RGP.2/23/42), Received by Fahd N. Al-Wesabi.