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

Reinforcement Learning (RL)
- Agent learns to maximize long-term spectrum utilization.
- Reward = throughput − interference penalty.
- Action = power levels, beamforming vectors, or subcarrier assignment.
Deep Learning / Neural Networks
- Predict interference in crowded spectrum.
- Map CSI to optimal transmit configuration.
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

Collect data: channel states, interference measurements, historical spectrum usage.
Train AI model: RL or supervised learning.
Decision-making: AI outputs optimal power/beamforming/frequency allocation.
Apply configuration: transmitters adapt in real time.
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.

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