AI-Driven Spectrum Sharing (with MATLAB)

1. Concept of AI-Driven Spectrum Sharing

AI-driven spectrum sharing uses artificial intelligence / machine learning to manage spectrum dynamically and efficiently:

• Multiple users or systems share the same frequency band.
• AI algorithms predict spectrum usage, interference patterns, and optimal allocation.
• The system adapts power, beamforming, or frequency allocation in real time based on learned patterns.

Goal: Maximize throughput, minimize interference, and improve spectrum utilization without human intervention.

2. Mathematical Model

Suppose:

• \(M\) transmitters share the same band.
• \(s_k(t)\) is the signal from transmitter \(k\).
• Channel from transmitter \(k\) to receiver \(m\): \(h_{mk}\).
• Noise at receiver \(m\): \(n_m(t)\).

Received signal:

$$ y_m(t) = \sum_{k=1}^{M} h_{mk} s_k(t) + n_m(t) $$

Interference at receiver \(m\):

$$ I_m = \sum_{k \ne m} |h_{mk}|^2 P_k $$

AI agent uses this data to predict interference patterns and decide optimal power or resource allocation:

$$ P^* = AI\text{-}model(H, I, S) $$

Where:

• \(P^* = [P_1^*, P_2^*, ..., P_M^*]\) → optimal transmit powers.
• \(H\) → channel state information (CSI).
• \(I\) → measured or predicted interference.
• \(S\) → spectrum usage statistics.

2.1 Optimization Formulation

The system can maximize sum rate while respecting interference constraints:

$$ \max_{P} \sum_{k=1}^{M} \log_2\left(1 + \frac{|h_{kk}|^2 P_k}{I_k + N_0}\right) $$

subject to

$$ I_m = \sum_{k \ne m} |h_{mk}|^2 P_k \le I_{th}, \quad \forall m $$ $$ 0 \le P_k \le P_{max}, \quad \forall k $$

Here, an AI model predicts optimal \(P_k\) for each transmitter. Similar formulations exist for beamforming weights in multi-antenna systems.

2.2 AI Techniques Used

1. Reinforcement Learning (RL)
   • Agent learns to maximize long-term spectrum utilization.
   • Reward = throughput − interference penalty.
   • Action = power levels, beamforming vectors, or subcarrier assignment.
2. Deep Learning / Neural Networks
   • Predict interference in crowded spectrum.
   • Map CSI to optimal transmit configuration.
3. Multi-Agent Systems
   • Each transmitter is an agent.
   • Agents coordinate spectrum usage using AI.

3. Practical Industry Use

• 5G NR and 6G networks: Dynamic spectrum allocation for multiple users.
• Cognitive radio networks: AI predicts primary user activity and allocates secondary user transmissions.
• IoT deployments: Large numbers of low-power devices sharing limited bands.
• Radar-communication coexistence: AI predicts radar pulses and adjusts communication signals to minimize interference.

4. Workflow Overview

1. Collect data: channel states, interference measurements, historical spectrum usage.
2. Train AI model: RL or supervised learning.
3. Decision-making: AI outputs optimal power/beamforming/frequency allocation.
4. Apply configuration: transmitters adapt in real time.
5. Feedback: measure interference and update AI model continuously.

5. Summary

Mathematical essence:

$$ (H, I, S) \rightarrow (P, \text{beamforming}, \text{frequency}) $$

• Optimizes spectrum usage while respecting interference constraints.
• Extends classical interference-driven spectrum sharing with prediction and learning.

Understanding AI-Driven Spectrum Sharing: H, I, S

In AI-driven spectrum sharing, the optimal transmit powers are predicted as:

$$ P^* = AI\text{-}model(H, I, S) $$

1. H — Channel State Information (CSI)

• Definition: \(H = [h_{mk}]\), where \(h_{mk}\) is the complex channel gain from transmitter \(k\) to receiver \(m\).
• Dimensions: \(M \times K\) (M receivers × K transmitters)

$$ y_m(t) = \sum_{k=1}^{K} h_{mk} s_k(t) + n_m(t) $$

\(h_{mk}\) encodes amplitude attenuation and phase shift.

2. I — Interference Measurement / Prediction

$$ I_m = \sum_{k \ne m} |h_{mk}|^2 P_k $$

AI uses \(I\) to predict interference patterns.

3. S — Spectrum Usage / Statistics

• Historical or current spectrum occupancy data.

$$ S = [s_1, s_2, ..., s_K] $$

4. Combining H, I, S

$$ (H, I, S) \rightarrow P^* $$

• \(P^* = [P_1^*, P_2^*, ..., P_K^*]\)

AI-based Power Allocation

P_opt = P_max*ones(1,M);

for k = 1:M
    if I(k) > I_th
        P_opt(k) = P_max * (I_th/I(k));
    end
end

Power scaling formula:

$$ P_k^{opt} = P_{max} \cdot \frac{I_{th}}{I_k} $$

Apply Power Allocation

s_new = sqrt(P_opt.').*s;

$$ s_k^{new}(t) = \sqrt{P_k^{opt}} \cdot s_k(t) $$

Power relationship:

$$ P = |s|^2 $$

Recalculate Received Signals

Y_new = H*s_new + n;

$$ y_m^{new}(t) = \sum_{k=1}^{M} h_{mk} s_k^{new}(t) + n_m(t) $$

Each receiver now receives adjusted signals with reduced interference.

 

Further Reading

1. MATLAB Code for AI-Driven Spectrum Sharing