Impact of Wireless Priors on the Computation and Energy Cost of MU-MIMO Precoding Learning

CONG Pengyu; HAN Shengqian; DENG Mingyu; LIU Shengjie; YANG Chenyang; SHEN Songhui

doi:10.11999/JEIT260388

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CONG Pengyu, HAN Shengqian, DENG Mingyu, LIU Shengjie, YANG Chenyang, SHEN Songhui. Impact of Wireless Priors on the Computation and Energy Cost of MU-MIMO Precoding Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260388

Citation:

CONG Pengyu, HAN Shengqian, DENG Mingyu, LIU Shengjie, YANG Chenyang, SHEN Songhui. Impact of Wireless Priors on the Computation and Energy Cost of MU-MIMO Precoding Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260388

Citation:

CONG Pengyu, HAN Shengqian, DENG Mingyu, LIU Shengjie, YANG Chenyang, SHEN Songhui. Impact of Wireless Priors on the Computation and Energy Cost of MU-MIMO Precoding Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260388

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Impact of Wireless Priors on the Computation and Energy Cost of MU-MIMO Precoding Learning

doi: 10.11999/JEIT260388 cstr: 32379.14.JEIT260388

1.
School of Electronic Information Engineering, Beihang University, Beijing 100191, China
2.
China Mobile Research Institute, Beijing 100053, China

Accepted Date: 2026-07-15
Rev Recd Date: 2026-07-15

Available Online: 2026-07-25

Abstract

Abstract

Objective This paper studies the learning of downlink MU-MIMO (Multiuser Multi-Input Multi-Output) precoding policy from the perspective of computational complexity and energy consumption. Traditional numerical optimization achieves strong performance but incurs rapidly growing computational complexity with the number of base-station’s antennas and served users, leading to high inference latency and inference energy consumption. In recent years, deep learning has been widely used to reduce online computational cost, yet existing evaluations typically rely on training/inference time or FLOPs and lack direct energy/power measurements. More importantly, the computational cost of a deep model is tightly coupled with the network architecture, which should be designed to effectively exploit the prior knowledge of the precoding policy. The objective of this paper is thus to develop a network architecture that matches the multi-dimensional permutational properties of the policy, and to analyze how policy priors influence the complexity and energy through comprehensive hardware-based energy/power measurements and simulations. Methods We formulate the MU-MIMO precoding policy as a mapping from multi-user channel information to the optimal precoding matrix under a transmit power constraint. The optimal policy exhibits multi-dimensional joint permutation equivariance and invariance with respect to user indices, receive-antenna indices, and base-station antenna indices. To exploit this prior, we propose an Attention-based Graph Neural Network (AGNN) built on a hypergraph structure, whose update and aggregation procedures are designed to satisfy the required equivariance/invariance properties. An attention mechanism modeling inter-user interference is introduced to improve generalization to varying number of users. For broadband precoding, the input layer aggregates multi-subcarrier channel information into an expanded input representation. To quantify energy cost, we build a mixed CPU/GPU measurement framework that collects energy and power for GPU, CPU, and DRAM during training and inference. Simulations use 3GPP TR 38.901 UMa channel data with different antenna array sizes and bandwidth settings. We compare the proposed AGNN against numerical baselines (ZFBD+Greedy pairing) and two Transformer-based architectures, where only one-dimensional permutation properties are satisfied. Results and Discussions : The paper reveals two main findings. First, partial exploitation of policy priors leads to poor performance and high cost. In the MU-MISO scenario, the Transformer variants with one-dimensional permutation equivariance yield lower spectral efficiencies than the numerical baseline ZFBD+Greedy and much larger model sizes and inference FLOPs than AGNN. In contrast, AGNN, designed to match the multi-dimensional permutation properties, achieves higher spectral efficiency while reducing inference FLOPs by about one order of magnitude. Hardware measurements further show that AGNN reduces inference energy and power on both CPU and GPU. Second, in the MU-MIMO scenario with small- and large-scale settings, ZFBD+Greedy increases the system sum rate to 10.9 times, but raise inference FLOPs to 436.4 times, inference time to 20.8 times, and inference energy to 40.8 times. Conversely, AGNN increases the sum rate to 11.5 times while inference FLOPs rise to only 5.3 times; inference time and inference energy become dramatically smaller (0.03 times and 0.22 times, respectively). These results suggest that matching the precoding policy’s multi-dimensional permutation priors is an effective way to reduce the computational cost, including FLOPs, latency, and energy consumption, in large-scale 6G MU-MIMO systems. Conclusions This paper investigated how policy priors affect computational complexity and energy consumption in MU-MIMO precoding learning. By analyzing the multi-dimensional joint permutation equivariance/invariance properties of the optimal precoding policy, we designed an attention-based GNN (AGNN) that matches these properties. A hardware-aware measurement platform was built to obtain direct energy/power measurements for training and inference on CPU, GPU, and DRAM components. Simulations on 3GPP TR 38.901 channel datasets showed that Transformer architectures satisfying only one-dimensional permutation properties can lead to both inferior spectral efficiency and significantly higher computational/energy overhead. In contrast, AGNN achieves higher spectral efficiency while substantially reducing inference FLOPs, inference time, inference energy, and training complexity. As system size increases, traditional numerical methods incur rapidly growing energy costs relative to rate gains, whereas the proposed policy-prior learning method maintains low inference time and energy consumption. Overall, exploiting the prior knowledge of MU-MIMO precoding policy for network architecture design is a key enabler for energy- and complexity-efficient high-dimensional precoding optimization in future 6G networks.
- 6G,
- Precoding,
- Policy priors,
- Graph neural networks,
- Computational energy consumption

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References(20)

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