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Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination
Faris B. Mismar, Brian L. Evans, Ahmed Alkhateeb
TL;DR
The paper addresses a joint optimization whose optimum requires exhaustive search, and proposes a deep-reinforcement-learning algorithm to avoid that search. The proposed algorithm outperformed tabular Q-learning and a fixed power allocation industry standard, while not requiring channel state information.
Problem
The joint optimization has no closed-form solution, and finding the optimum requires an exhaustive search with runtime exponential in the finite-set size.
Method
The paper develops a joint beamforming, power control, and interference coordination algorithm using deep reinforcement learning.
Results
The proposed algorithm outperformed both tabular Q-learning and the industry-standard fixed power allocation algorithm.
Takeaways & Limitations
The algorithm removes the need for channel state information, avoiding channel estimation and its associated training.
Takeaways & Limitations
The study’s setting includes an assumption about each base station’s capability.
Abstract
from arXiv · showhide
The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum-rate capacity, but with less than 4% of the required run time.
I. INTRODUCTION
The paper addresses joint beamforming, power control, and interference coordination for improving 5G communication performance. It proposes an online reinforcement-learning algorithm for voice and data bearers while studying centralized information overhead.
- 5G networks must accommodate increasing traffic and data-rate demands alongside improved voice-call reliability.
- The framework targets end-user SINR and sum-rate capacity as communication-performance objectives.
- The central problem is jointly optimizing power control, interference coordination, and beamforming without exhaustive action-space search.
- The proposed algorithm uses reinforcement learning to explore the joint solution space through interaction for both voice and data bearers.
- The study also examines centralized online learning overhead when information is passed to a central location.
A. Related Work
Prior work studied power control, beamforming, interference coordination, and reinforcement learning separately or in narrower settings. The reviewed approaches motivate joint downlink optimization while exposing limitations in overhead, propagation modeling, and computational cost.
- Earlier studies examined joint uplink or downlink power control and beamforming, often targeting SINR or achievable sum rate.
- Related mmWave optimization considered SINR without accounting for scattering or shadowing, which are critical propagation phenomena.
- Industry ABS methods resolve LTE co-channel interference, but dynamic beamforming reduces their usefulness compared with fixed beam patterns.
- Reinforcement-learning approaches addressed voice power control, interference avoidance, and uplink optimization, but uplink learning can be computationally expensive and deplete UE batteries faster.
- The related-work table compares the paper’s approach with earlier methods across bearer, objective, procedure, and algorithm categories.
low SINRs.
Prior deep-learning and reinforcement-learning studies addressed mmWave beam prediction, beamforming, and voice bearers in specialized settings. Their differences include channel-information requirements, omitted power or interference control, and restricted deployment environments.
- Other studies reduced channel mapping requirements or used neighboring-base-station signals for beam prediction and overhead reduction.
- Adversarial reinforcement learning produced near-optimal SINR antenna diagrams but did not address power control or interference coordination.
- Earlier voice-bearer work considered a single co-located base-station environment rather than the multi-base-station setting addressed here.
- Deep-neural-network beamforming methods can require channel knowledge to make decisions.
B. Motivation
The motivation is to replace exhaustive, channel-dependent joint optimization with a lower-feedback solution for coordinated multi-base-station downlink control. The proposed design uses user coordinates and received SINR while recognizing implementation and standards constraints.
- Centralized control includes serving and interfering base-station transmit powers, creating a race condition when one base station serves one user but interferes with another.
- The proposed solution avoids channel knowledge when finding the SINR-optimal beamforming vector.
- The UE reports received SINR and coordinates, while the agent issues power-control and interference-coordination commands to involved base stations.
- Using UE coordinates instead of channel-state information reduces reporting overhead.
- Passing explicit PCIC commands to serving and interfering base stations can be prohibitively complex and requires modification of current industry standards.
C. Contributions
The paper formulates joint downlink beamforming, power control, and interference coordination as a non-convex optimization problem and develops a deep reinforcement learning solution. It targets voice bearers in sub-6 GHz networks and data bearers in mmWave networks, using user-reported SINR and coordinates to coordinate base stations.
- The paper formulates joint beamforming, power control, and interference coordination to optimize users’ received SINR.
- A central location handles coordination between base stations using user-reported downlink SINR and coordinates.
- The proposed deep reinforcement learning solution supports multiple actions at once through binary encoding of base-station actions.
- Voice bearers operate in sub-6 GHz bands, while data bearers use mmWave bands with analog beamforming to compensate for higher propagation loss.
- The optimization is non-convex because its first two constraints are non-convex.
- The voice algorithm improves effective SINR and reduces voice packet error rate.
C. Proposed Algorithm
Algorithm 1 uses deep Q-learning to jointly select beamforming, power-control, and interference-coordination actions based on SINR outcomes. It trains from observed transitions and rewards actions according to effective SINR targets and constraints.
- Algorithm 1 is a DRL-based approach that jointly optimizes beamforming, power control, and interference coordination.
- DQN may reduce computational overhead relative to tabular Q-learning depending on the number of states and network depth.
- The algorithm selects a joint action, observes its SINR impact, assigns a reward, and trains the DQN from the outcome.
- The same power-control commands can represent serving-BS power control or interfering-BS coordination depending on the BS role.
- The action output is a sequence of beamforming, power-control, and interference-coordination commands driven by downlink SINR measurements.
A. Proposed Algorithm
The proposed data-bearer algorithm jointly controls beamforming and transmit power, while using UE coordinates and received SINR rather than full channel-state reporting. Its runtime is substantially lower than exhaustive search, whose complexity grows as O(ML).
- The DRL algorithm jointly controls beamforming vectors and transmit powers at the base stations to maximize the objective function.
- A bit-string action register enables several beamforming, power-control, and interference-coordination actions to occur concurrently.
- The agent changes beamforming codebook indices and transmit powers while monitoring the resulting SINR.
- Reporting UE coordinates and received SINR reduces overhead from M complex-valued channel elements to two real-valued coordinates plus SINR.
- The brute-force search becomes prohibitive for large antenna arrays because its runtime is O(ML), exceeding the proposed algorithm's runtime.
- The proposed algorithm's runtime lacks a comparable closed-form complexity expression because deep Q-learning has no convergence and stability guarantees.
VIII. SIMULATION RESULTS
The simulation section evaluates the proposed reinforcement-learning solutions using the performance measures defined earlier. It presents the adopted setup before the simulation results.
- The study evaluates its RL-based solutions using the performance measures defined for the experiments.
- The evaluation first describes the network setup and then presents simulation results in Sections VIII-B and VIII-C.
A. Setup
The setup models users in an urban cellular environment with mobility, fading, and line-of-sight conditions, and defines bearer-specific targets and discrete joint actions. Actions are encoded as bit registers and rewarded according to SINR objectives and constraints.
- Users are uniformly distributed in the coverage area and move at speed v under log-normal shadow fading and small-scale fading.
- The setup uses adaptive voice coding, fixed QPSK modulation, and target effective SINRs with a minimum SINR of −3 dB.
- The state includes each UE's Cartesian coordinates, while actions are constructed from binary encodings of beamforming and power-control choices.
- For voice bearers, the action register selects discrete power changes for serving and interfering base stations.
- For data bearers, the register selects ±1 dB power changes and beamforming codebook steps for both base stations.
- The power-offset set is P = {±1, ±3} dB, motivated by industry-standard increments and preservation of problem non-convexity.
- Rewards depend on action relevance and whether target or minimum SINR conditions are met; episodes are aborted when constraints become inactive.
B. Outcomes
The proposed algorithm’s convergence and runtime vary with antenna count, while coverage and sum-rate improve as the number of antennas increases.
- Convergence: The number of episodes required for convergence increases with M, with minimal effect from the constant threshold γbf_0.The number of codebook-traversal attempts grows almost linearly with M.
- Run time: As M increases, the proposed algorithm’s run-time complexity increases because it searches through more beams.The number of beams required for the search increases with M.
- Coverage: Voice-bearer coverage improves across the SINR CCDF, while data-bearer coverage improves where SINR increases monotonically with M.The data-bearer trend is attributed to increasing beamforming array gain.
- Sum-rate: Sum-rate capacity increases logarithmically with M.The increase is justified using the paper’s rate expressions in (5) and (15).
C. Figures
The figures compare coverage, convergence, runtime, SINR, transmit power, and sum-rate for the proposed methods against brute-force, tabular Q-learning, and FPA baselines.
- Algorithm comparison: Deep Q-learning outperforms tabular Q-learning because it achieves a higher reward and converges to a solution identical to brute force.Tabular convergence may be impeded by the initialization of the state-action value function.
- Coverage: Near γeff = 13 dB, users are close to the base-station center and power-control algorithms perform almost similarly.The distinction among algorithms is therefore most relevant below this effective-SINR regime.
- Coverage: As M increases, effective-SINR coverage improves because effective SINR depends on beamforming array gain.The coverage comparison is shown in Fig. 7.
- Runtime: The proposed algorithm has significantly lower runtime than brute force, whose runtime grows more steeply with M.At M = 4, the proposed algorithm requires only 4% of brute force runtime.
- Convergence: At large M, convergence requires more episodes because the agent searches narrower beam grids for the target SINR.This longer convergence time can negatively affect data throughput and voice frames.
- Convergence: Convergence delay reduces data throughput to b/T_bfζ and causes ⌈νζ⌉ lost voice frames.The delay reflects time spent using sub-optimal beams during earlier iterations.
- SINR and power: Achieved SINR is proportional to M, while JB-PCIC approaches brute-force performance across antenna sizes.The transmit power is almost equal to the maximum, and the performance gap diminishes across M.
- Sum-rate: JB-PCIC’s sum-rate capacity approaches the upper performance limit, with the gap diminishing across M.The sum-rate comparison is shown in Fig. 11.
IX. CONCLUSION
The paper develops a deep-reinforcement-learning algorithm for joint beamforming, power control, and interference coordination without requiring channel-state knowledge.
- Problem: The joint optimization has no closed-form solution, and exhaustive search has runtime exponential in the search space.This motivates a learning-based alternative for the multi-antenna cellular setting.
- Method: JB-PCIC uses deep reinforcement learning at a central location receiving UE measurements over the backhaul.The algorithm jointly selects beamforming, power-control, and interference-coordination actions.
- Results: For voice bearers, the proposed algorithm outperformed tabular Q-learning and fixed power allocation.Voice uses sub-6 GHz frequencies, while data uses mmWave frequencies.
- Scope boundary: The method requires UE coordinates and received SINR to be sent to the base station every millisecond.This feedback requirement defines an operational boundary for deployment.
- Practical implications: The proposed solution does not require channel knowledge, removing channel estimation and associated training sequences.UE feedback consists of coordinates and received SINR rather than explicit beamforming, power-control, or interference-coordination commands.