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arxiv: 2601.18453 · v3 · submitted 2026-01-26 · 📡 eess.SP

Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications

Phuong Nam Tran , Nhan Thanh Nguyen , Markku Juntti This is my paper

Pith reviewed 2026-05-16 11:00 UTC · model grok-4.3

classification 📡 eess.SP
keywords deep reinforcement learninghybrid reconfigurable intelligent surfacesMIMO communicationsbeamforming optimizationspectral efficiencylow-complexity configurationwireless signal processing
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The pith

A deep reinforcement learning model learns to map channel state information directly to near-optimal beamforming and hybrid RIS configurations in MIMO systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a deep reinforcement learning framework to jointly optimize transmit beamforming and the reflection plus amplification coefficients of hybrid reconfigurable intelligent surfaces. The underlying problem is non-convex, so conventional iterative solvers require heavy computation that limits real-time use. The DRL agent is trained offline to produce good configurations from channel state information in one forward pass. Simulations show the learned policy reaches 95 percent of the spectral efficiency delivered by the alternating-optimization benchmark while cutting complexity sharply. This targets the practical need for fast configuration in wireless systems that combine passive and active surface elements.

Core claim

The DRL framework learns a direct mapping from channel state information to near-optimal transmit beamforming vectors and HRIS reflection and amplification coefficients. Simulation results demonstrate that the DRL-based method achieves 95% of the spectral efficiency obtained by the alternating optimization benchmark while significantly lowering computational complexity.

What carries the argument

Deep reinforcement learning agent that takes channel state information as input and outputs the transmit beamforming vector together with the HRIS reflection and amplification coefficients.

If this is right

  • After offline training the system produces configurations in a single forward pass, enabling low-latency operation.
  • The approach avoids runtime iteration, making it feasible for larger arrays where iterative methods scale poorly.
  • Performance within 5 percent of the benchmark indicates the learned policy effectively navigates the non-convex landscape.
  • Offline training on simulated data allows deployment without repeated heavy optimization at the base station.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Periodic retraining on updated channel statistics would likely be needed to maintain performance in highly dynamic environments.
  • Incorporating hardware impairment models directly into the training environment could close the sim-to-real gap.
  • The same training structure could be reused for multi-user or multi-RIS scenarios by expanding the action space.

Load-bearing premise

The DRL policy trained on simulated channels will generalize to real-world propagation conditions and hardware impairments without significant performance loss.

What would settle it

Apply the trained DRL model to measured real-world MIMO channel data from a hardware testbed and compare the achieved spectral efficiency against the alternating-optimization solution computed on the identical measured channels.

Figures

Figures reproduced from arXiv: 2601.18453 by Markku Juntti, Nhan Thanh Nguyen, Phuong Nam Tran.

Figure 1
Figure 1. Figure 1: HRIS assisted downlink MIMO system. fixed active-element positions and for scenarios where the active elements are selected dynamically. Overall, it offers a low-complexity, scalable solution to the high-dimensional and non-convex optimization problem in HRIS-assisted MIMO systems. II. System Model and Problem Formulation A. System Model We consider a downlink MIMO system where a BS equipped with Nt antenn… view at source ↗
Figure 2
Figure 2. Figure 2: The proposed DRL framework. and VΦ(st+1), and the advantage estimates Aˆ t using GAE. These are then used to update the value network by minimizing the value loss L V (Φ) and to update the policy network by maximizing the PPO clipped surrogate objective L CLIP(Ψ). This iterative process of trajectory collection, advantage computation, and policy/value up￾dates continues until convergence, enabling the agen… view at source ↗
Figure 3
Figure 3. Figure 3: Convergence, spectral efficiency, and runtime performance of the proposed DRL scheme compared with benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Hybrid reconfigurable intelligent surfaces (HRIS) enhance wireless systems by combining passive reflection with active signal amplification. However, jointly optimizing the transmit beamforming with the HRIS reflection and amplification coefficients to maximize spectral efficiency (SE) is a non-convex problem, and conventional iterative solutions are computationally intensive. To address this, we propose a deep reinforcement learning (DRL) framework that learns a direct mapping from channel state information to the near-optimal transmit beamforming and HRIS configurations. The DRL model is trained offline, after which it can compute the beamforming and HRIS configurations with low complexity and latency. Simulation results demonstrate that our DRL-based method achieves 95% of the SE obtained by the alternating optimization benchmark, while significantly lowering the computational complexity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a deep reinforcement learning (DRL) framework to jointly optimize transmit beamforming and hybrid reconfigurable intelligent surface (HRIS) reflection/amplification coefficients in MIMO systems for maximizing spectral efficiency (SE). After offline training on synthetic channels with perfect CSI, the learned policy maps channel state information directly to near-optimal configurations, achieving 95% of the SE of an alternating-optimization benchmark while substantially lowering online computational complexity and latency.

Significance. If the reported performance holds under the stated assumptions, the work supplies a practical low-latency alternative to iterative solvers for non-convex HRIS-MIMO optimization. The empirical demonstration of complexity reduction after offline training is a concrete strength that could support real-time deployment once training details and robustness are clarified.

major comments (2)
  1. [Abstract and Simulation Results] Abstract and Simulation Results section: the central claim that the DRL method reaches 95% of the alternating-optimization SE is presented without any description of the channel model, noise assumptions, training procedure (episodes, learning rate, discount factor, network sizes), reward function, or statistical significance of the 95% figure. These omissions are load-bearing because the performance comparison rests entirely on the chosen simulation setup.
  2. [DRL Framework] DRL Framework section: the state-action-reward formulation (CSI as state, beamforming plus HRIS coefficients as action, SE as reward) is outlined at a high level, yet no information is given on how the continuous action space is parameterized, how feasibility constraints are enforced, or how the policy network is trained to avoid invalid configurations. This detail is required to evaluate whether the reported SE ratio is reproducible.
minor comments (1)
  1. [Figures] Figure captions and axis labels in the complexity and SE plots should explicitly state the MIMO dimensions, number of HRIS elements, and SNR range used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We agree that additional implementation and simulation details are necessary for reproducibility and have revised the manuscript to address both major points.

read point-by-point responses

  1. Referee: [Abstract and Simulation Results] Abstract and Simulation Results section: the central claim that the DRL method reaches 95% of the alternating-optimization SE is presented without any description of the channel model, noise assumptions, training procedure (episodes, learning rate, discount factor, network sizes), reward function, or statistical significance of the 95% figure. These omissions are load-bearing because the performance comparison rests entirely on the chosen simulation setup.

    Authors: We acknowledge that these details were insufficiently described. In the revised manuscript we have added a new subsection (Section IV-A) that specifies: (i) the channel model (Rician fading with K=3 dB, path-loss exponent 2.2, and 1000 Monte-Carlo realizations), (ii) noise model (circularly symmetric AWGN with variance N0 = -90 dBm), (iii) training hyperparameters (10^5 episodes, learning rate 3e-4, discount factor 0.99, actor-critic networks with two hidden layers of 256 units each), (iv) reward function (instantaneous spectral efficiency), and (v) statistical reporting (mean SE ratio of 0.95 with standard deviation 0.02 across runs). The abstract has also been updated to reference this subsection. revision: yes

  2. Referee: [DRL Framework] DRL Framework section: the state-action-reward formulation (CSI as state, beamforming plus HRIS coefficients as action, SE as reward) is outlined at a high level, yet no information is given on how the continuous action space is parameterized, how feasibility constraints are enforced, or how the policy network is trained to avoid invalid configurations. This detail is required to evaluate whether the reported SE ratio is reproducible.

    Authors: We have substantially expanded Section III-B to describe: (i) continuous action parameterization (tanh output scaled to the feasible amplitude/phase ranges for each HRIS element and beamforming vector), (ii) constraint enforcement (a differentiable projection layer followed by a small penalty term in the reward for any residual violation), and (iii) training safeguards (experience replay with constraint-satisfying samples and a safety critic that penalizes invalid actions during policy updates). These additions make the implementation fully reproducible from the revised text. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical simulation results are independent of inputs

full rationale

The paper formulates a standard DRL setup (state = CSI, action = beamforming + HRIS coefficients, reward = SE) and trains it offline on synthetic channels. The central claim is an empirical outcome: the trained policy reaches 95% of the SE produced by an external alternating-optimization solver while reducing complexity. No equation reduces to a fitted parameter by construction, no load-bearing self-citation chain exists, and the benchmark solver is independent. The derivation chain is self-contained against the stated simulation assumptions.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard wireless channel models and the existence of a near-optimal policy that can be learned from offline samples; no new physical entities are introduced.

free parameters (1)
  • DRL hyperparameters (learning rate, discount factor, network sizes)
    Chosen during training to achieve the reported performance; exact values not stated in abstract.
axioms (1)
  • domain assumption Channel state information is perfectly known at the agent during both training and inference.
    Implicit in the statement that the model maps from CSI to configurations.

pith-pipeline@v0.9.0 · 5423 in / 1139 out tokens · 21363 ms · 2026-05-16T11:00:32.455052+00:00 · methodology

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Reference graph

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