Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

Area of Science:

• Wireless communication networks
• Signal processing in telecommunications

Background:

• Orthogonal frequency division multiple access (OFDMA) is crucial for wireless sensor networks, enabling user-specific subcarrier allocation based on channel gains.
• Optimizing subcarrier and power assignment in OFDMA systems is vital for maximizing system sum rate.

Purpose of the Study:

• To address the NP-hard and non-convex resource allocation problem in OFDMA systems.
• To propose an efficient algorithm for rate-adaptive resource allocation with proportional fairness constraints.

Main Methods:

• A novel algorithm, ACO-SPA, combining Ant Colony Optimization (ACO) for subcarrier allocation and Suboptimal Power Allocation (SPA) is developed.
• The resource allocation problem is decomposed into two steps to reduce computational complexity.
• SPA is based on equalizing the sum of power and the reciprocal of channel-to-noise ratio across subchannels for each user.

Main Results:

• The proposed ACO-SPA algorithm demonstrates superior performance compared to traditional root-finding and linear methods.
• Simulation results validate the effectiveness of the algorithm in solving proportional resource allocation problems.
• The method achieves high system sum rates while adhering to proportional fairness constraints.

Conclusions:

• ACO-SPA offers an efficient and effective solution for resource allocation in OFDMA systems.
• The algorithm successfully balances system performance (sum rate) with fairness requirements.
• This approach provides a valuable contribution to optimizing OFDMA network performance.