All classes 5G Deep RL Resource Allocation Syllabus · Episode 6 of 6

Class 5G Resource Allocation with Deep RL #6: Throughput, Fairness, and Energy Efficiency Evaluation

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Tutorial evaluating DQN agent resource allocation 5G against round robin, proportional fair, and water-filling: throughput, fairness, energy efficiency. Episode 6, closing the series of 5G deep reinforcement learning resource allocation.

Software: Python (NumPy, PyTorch, Gymnasium, Matplotlib)
Level: Advanced
Duration: ± 8 min
Series total: 1 h 1 min · 6 videos
Published: October 25, 2026

Episode 6 of 6 · view syllabus

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Introduction

This is the final result, the QN agent is tested on 5 unseen networks. Its energy efficiency is about 20% higher than proportional fair with almost the same fairness, because the agent learns on its own when low power is already sufficient. Let's start. Hello, welcome to the final episode of the 5G resource allocation series with Deep Reinforcement Learning. Honest evaluation is the core of research. The agent must be tested on networks it has never seen during training. Compared with strong baselines, and its advantages and disadvantages are observed. Today we do that.

Step 1: Five schedulers in the test network

Create an evaluation .py file We load the trained The QN weights, then run the agent greedily, without exploration, for 1000 slots on 5 test networks with SID 101-105 that never appeared during training. We also record the selected SINR user and the power level used. The four baselines are run on the exact same network and channel, then their metrics are averaged. Run Max C per I remains the fastest, about 23 Mbps, but is unfair. Proportional fair is about 17.95 Mbps with JIN 0.835. The QN achieves about 16 Mbps with JIN almost 0.8, slightly below PF, but its energy efficiency is about 20% higher, approaching Max C per I while remaining fair.

Step 2 Throughput, energy & power options

Continue with the graphs, throughput and energy efficiency of all schedulers, as well as the histogram of The QN's power selection against SINR user. Let's look at the graphs. The right histogram reveals what the agent learned, for users with SINR above 30dB, which are already saturated at the 256KM boundary, The QN selects 25% power, whereas for weak users it uses full power. The agent discovers on its own the principle we saw in the first episode, without ever being told. Its cost, throughput drops about 10%, a trade-off that can be adjusted via the beta parameter in the reward.

Done!

Done, and the series is complete. We have built a complete 5G resource allocation research, including the concept of resource block, channel simulator and SINR, round robin scheduler, proportional fair, and water filling, MDP formulation, deep Q network from scratch, up to throughput, fairness, and energy efficiency evaluation. For further research, try other algorithms such as PPO or multi-agent RL for multiple cells, Maimo channels, underloaded traffic, or networks leaching in 6G. Thank you for following this series. Currently working on a thesis, dissertation, or research in the field of telecommunications and artificial intelligence? Cerdas Inovasi Digital helps implement network research, from 5G simulators, channel models and SINR, proportional fair and water filling scheduling, up to reinforcement learning with PyTorch, and also opens Python programming courses for electrical engineering and telecommunications. Want to master the entire series, from resource block concepts to agent efficiency evaluation in The QN? All 6 episodes, complete with PDF step-by-step guides and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy single classes with lifetime access. Subscribe now at the premium page cerdasinovasidigital.com Contact us via WhatsApp, or visit cerdasinovasidigital.com Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:43Step 1: Five schedulers in the test network

4:47Step 2 Throughput, energy & power options

6:34Done!

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Transcript

0:03Introduction

0:43Step 1: Five schedulers in the test network

4:47Step 2 Throughput, energy & power options

6:34Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Five schedulers in the test network

Step 2 Throughput, energy & power options

Done!

Introduction

Step 1: Five schedulers in the test network

Step 2 Throughput, energy & power options

Done!