I. Overview

In the era of the Internet of Things (IoT), billions of devices are connected via wireless networks, resulting in a complex interplay of various wireless signals within limited spectrum resources. It is predicted that the number of connected IoT devices globally will exceed 75 billion by 2025 and 25 billion by 2030. The sheer number of devices operating within a limited spectrum exacerbates the problem of interference.

II. Co-channel and Adjacent-channel "Signal Conflicts"

Co-channel interference refers to the mutual interference between two IoT devices operating on the same frequency.

IoT devices often operate in the ISM band (including 2.4GHz and 5GHz). Taking the 2.4GHz band as an example, channels that do not interfere with each other are very limited. When multiple IoT devices operate on the same frequency and their coverage areas overlap, co-channel interference occurs.

Adjacent-channel interference stems from the spectral characteristics of wireless signals. The spectrum of wireless signals follows a bell-shaped distribution; outside the transmission bandwidth, the signal gradually decays rather than suddenly dropping to zero.

If the transmission bandwidths of two devices with different center frequencies overlap, adjacent-channel interference will occur. Even using non-overlapping adjacent channels, interference can still occur if the devices are too close together and have high transmission power.

In the IoT environment, multiple wireless technologies (such as Wi-Fi, Bluetooth, ZigBee, etc.) share the same frequency band, and the operation of non-communication devices such as microwave ovens and cordless phones further complicates the adjacent-channel interference problem.

III. Resource Constraints and High-Density Deployment

Compared to ordinary communication devices, IoT devices face unique challenges in dealing with interference:
Constrained Device Resources:** Many IoT devices have strict limitations on cost and power consumption, making it impossible to incorporate complex anti-interference algorithms like those found in mobile phones. Narrowband systems like NB-IoT have limited processing power, making it difficult to initially identify interference sources through interference spectrum characteristics.

High-Density Deployment:** In scenarios such as smart cities and smart agriculture, a large number of IoT devices are deployed densely. In densely deployed scenarios (such as smart city sensor clusters), the packet loss rate of LoRa master modules increases significantly.

Hidden Terminal Problem:** When two IoT devices are outside each other's transmission range but both communicating with the same access point, a hidden node problem arises. Their transmitted data may collide at the access point, leading to data transmission failure.

Near-Far Effect:** When an IoT device close to the base station and a device far away simultaneously transmit signals, the strong signal from the closer device may overwhelm the weak signal from the farther device.

IV. System-Level Impact

Interference has a comprehensive impact on IoT systems, ranging from performance degradation of individual devices to complete system service interruption.

In terms of performance metrics, interference reduces the signal-to-noise ratio (SNR) of IoT devices. When the SNR falls below 15dB, communication quality deteriorates significantly. Interference also increases the transmission error rate; in NB-IoT systems, interference is considered present when the noise floor is above -128dBm.

At the system level, interference reduces network capacity and limits coverage. In high-interference environments, the effective coverage radius of base stations shrinks. Interference also increases packet loss rate; in densely deployed LoRa networks, packet loss rates can exceed 30%.

At the application level, interference can cause IoT service interruptions. In industrial IoT environments, electromagnetic interference generated by numerous motors, welding machines, and other equipment can interfere with wireless monitoring and communication equipment in workshops, affecting the normal operation of production processes.

V. Anti-interference Technologies and Solutions

To address the challenges of interference, IoT systems can employ various anti-interference technologies:

Spectrum Management: Cognitive radio combined with reinforcement learning algorithms can detect spectrum holes in real time and achieve intelligent switching.

Dynamic channel allocation technology allows IoT devices to automatically select channels with less interference.

Physical Layer Technology: LoRa employs spread spectrum modulation (CSS) with a co-channel rejection capability of up to 19.5dB, enabling decoding even when signal strength is below noise levels. Orthogonal spreading factor (SF) technology ensures that signals with different SFs do not interfere with each other in the same frequency band.

Network Layer Optimization: LoRaWAN supports Adaptive Data Rate (ADR), dynamically adjusting SF, bandwidth, and coding rate to balance transmission distance and network capacity, reducing collision probability. Properly planned gateway density (e.g., deploying ≥3 gateways per square kilometer) can improve spatial diversity gain.

Hardware Design: Electromagnetic compatibility design is optimized through shielding, filtering, and grounding techniques. Based on Schelkunoff's shielding theory, the shielding effectiveness of a metallic shield is closely related to the material's conductivity, permeability, and frequency.