The joint optimization of user association and resource allocation (UARA) is a fundamental challenge in modern wireless networks, essential for balancing performance, user fairness, and efficiency under growing service demands. Given the latency constraints of emerging applications, distributed pricing-based strategies have widely replaced complex centralized approaches. However, existing literature on <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fairness predominantly assumes a homogeneous context, assigning an identical parameter <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> to all users. This rigidity fails to address the differentiated prioritization required by real-world networks with diverse application requirements. To bridge this gap, we propose a novel heterogeneous alpha-fairness (HAF) objective function. By assigning distinct <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> values to different users, our framework enables precise, user-specific control over the trade-off between throughput, fairness, and latency. We develop a distributed optimization algorithm utilizing an auxiliary variable framework and provide a rigorous analytical proof of its convergence to an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-optimal solution. Furthermore, we theoretically show that the optimal solution satisfies a generalized fairness condition that reduces to Kelly’s proportional fairness when <inline-formula> <tex-math notation="LaTeX">$\alpha =1$ </tex-math></inline-formula> for all users. Numerical results demonstrate that the proposed HAF method significantly outperforms conventional homogeneous schemes, offering superior flexibility and performance across multiple criteria in heterogeneous network environments.
Jonggyu Jang, Hyeonsu Lyu, D. J. Love et al.· IEEE Transactions on Communi...· 0 citations
Efficient Channel State Information (CSI) feedback is indispensable for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Existing compressed sensing (CS) algorithms exploit delay-domain sparsity but suffer from prohibitive iterative latency and discrete grid mismatch. Conversely, deep learning (DL) approaches achieve rapid inference but lack spatial scalability and domain adaptability, failing to generalize to unseen propagation environments, and demand computationally heavy encoders and decoder. In this paper, we propose TAP, a Tap-Assisted Parametric CSI Compression. TAP is a one-shot neural framework that unifies the speed of DL with the mathematical interpretability of CS. TAP replaces iterative pursuit with a lightweight 1D neural network that extracts dominant continuous propagation delays from temporal channel sequences via a differentiable sub-grid interpolation operator. TAP achieves true architecture independence, enabling zero-shot generalization across diverse array geometries and unseen propagation environments. Furthermore, TAP yields a completely decoder-free payload, allowing the BS to reconstruct the channel via a simple inverse fast Fourier transform (IFFT). Extensive evaluations across five 3GPP environments demonstrate that TAP achieves a 3.13 to 12.22 dB channel frequency response normalized mean square error (CFR-NMSE) improvement over CsiNet while shrinking the model footprint by 660 times to under 1 MB. Operating with sub-millisecond latencies, TAP accelerates inference by 2700 times over classical iterative OMP, providing a scalable and deployment-ready solution for next-generation networks.
Minwoo Kim, Hyeonsu Lyu, Sehyun Ryu et al.· 0 citations
LUCID is presented, an LLM-agent--orchestrated, uplink-aware cloud-robotics pipeline that moves TP--RRM from solving a fixed formulation to dynamically orchestrating optimization problem schemas within a DITL environment and robustly adapts to changing intents, active-robot counts, and scenes.
Hyeonsu Lyu, Minwoo Kim, Sehyun Ryu et al.· 0 citations
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