Link adaptation in 5G wireless networks faces fundamental challenges due to the stochastic nature of the radio environment. Deep reinforcement learning (DRL) has shown potential for automating adaptive control, but it often suffers from instability when user mobility, fading, and interference introduce significant variability in link performance. This inconsistency in action outcomes, particularly when adjusting the downlink target block error rate (BLER), complicates the learning process and slows convergence. To address this, we propose a probabilistic representation learning framework in which the variance of each user’s latent state is conditioned on the selected BLER. This structure enables the DRL agent to reason about action-dependent uncertainty and learn more robust, risk-aware policies. We implement this representation within DRL agents and evaluate the approach using a commercial-grade 5G system-level simulator based on ns-3. Experimental results show that the proposed method improves downlink throughput by up to 19.0% over fixed-rule baselines, achieves up to 11.3% gain over heuristic eOLLA algorithms, and reduces convergence time by 46.2%, consistently demonstrating superior performance under diverse mobility scenarios.

Fully Homomorphic Encryption (FHE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. Many companies maintain extensive, high-quality databases to deliver services, making preserving data privacy during the database pattern searches crucial. With FHE, the server can take encrypted queries from clients and search through the reference database on the server without decryption, thus guaranteeing data security for all parties. While FHE provides a promising solution to data privacy, it has severe drawbacks of explosive memory requirements and excessive latency, which amplify the computational and memory inefficiencies for database search applications.To address these, we propose PATHE that exploits FHE and hyperdimensional computing (HDC), which provides high parallelism, excellent robustness to errors, for high-performance privacy-preserving database search. On the software side, we propose an FHE-friendly PATHE algorithm that leverages efficient FHE-HDC search and a scheme-switching-based argmax to support database search and maintain comparable accuracy to the state-of-the-art. On the hardware side, PATHE proposes an efficient and scalable FHE accelerator system using Compute Express Link (CXL) for large-scale FHE database search, along with a novel, storage-aware dataflow designed to optimize memory and storage transfers for large database workloads. We evaluate PATHE on the large-scale encrypted database of protein mass spectra, PATHE achieves 2.1× speedup and 1.7× better energy efficiency compared to the baseline system.

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs—graph representations of activity history on a system—to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS.

Fully homomorphic encryption (FHE) is crucial for post-quantum privacy-preserving computing. Researchers have proposed various FHE schemes that excel at different encrypted computations, such as single-instruction multiple-data (SIMD) arithmetic or arbitrary single-data functions. Hybrid-scheme FHE, which exploits appropriate schemes for specific tasks, is essential for real-world applications requiring optimal performance and accuracy. However, existing FHE accelerators only adopt scheme-specific custom designs, leading to inefficiency or lack of capability to support applications in hybrid FHE settings. In this work, we propose a Unified FHE aCcelerator (UFC) that provides better performance and cost-efficiency than prior scheme-specific accelerators on hybrid FHE applications. Our design process involves a comprehensive analysis of processing flows to abstract the primitives covering all operations in hybrid FHE applications. The UFC architecture primarily comprises hardware function units for these primitives, diverging from the deeply pipelined units in previous designs. This approach enables high hardware utilization across different FHE schemes. Further-more, we propose several algorithm-hardware co-optimizations to minimize the hardware cost of supporting various data shuffling patterns in FHE. This enables high-throughput implementation of function units that provide good cost efficiency. We also propose several compiler-level optimizations to achieve high hardware utilization of the unified architecture for computing FHE data in various algorithmic parameter settings. We evaluate the performance of UFC on different FHE programs, including scheme-specific and hybrid-scheme workloads. Our experiments show that UFC provides up to 6.0 × speedup and 1.6 × delay-energy-area efficiency improvement over state-of-the-art FHE accelerators.

Statistical analysis of largescale data is useful as it enables the extraction of a large amount of information, despite its simplicity. Therefore, fusing and analyzing data from different security domains is an attractive and promising approach, unless it jeopardizes the privacy of the data in any security domain. In this study, we proposed the HEaaN-STAT toolkit that can efficiently fuse data from different domains to enable largescale statistical analysis while protecting data privacy. Moreover, we proposed an efficient inverse operation and a table lookup function for Cheon-Kim-Kim-Song (CKKS) encrypted data, as well as a data encoding method for counting encrypted data. Based on this, we proposed a method for generating a contingency table with a large number of cases and k-percentile for largescale data that is hundreds to thousands of times faster than the method proposed by Lu et al. in NDSS’17. The validity of the proposed toolkit was verified through practical use for business applications using real-world data.

As technologies for 4G and 5G networks become increasingly complex, user equipment's (UE) energy consumption also increased significantly. The 4G and 5G systems employ connected-mode discontinuous reception (C-DRX) to save UEs energy consumption by intermittently suspending network connections. However, optimizing the C-DRX operation is complex that requires considering various network conditions, including traffic patterns of each UE and the scheduling algorithms of a base station (BS). In this paper, we introduce a novel approach, a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm with an integrated self-attention mechanism. This approach empowers the model to optimize C-DRX parameters independent of BSs' scheduling algorithms, enhancing its adaptability to dynamically changing network conditions. Simulation results illustrate that our MADRL-based C-DRX optimization algorithm consistently outperforms traditional methods under varying network conditions, affirming its robustness and efficacy for real- world network optimization.

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

Fully Homomorphic Encryption (FHE) enables arbitrary computations on encrypted data without decryption, thus protecting data in cloud computing scenarios. However, FHE adoption has been slow due to the significant computation and memory overhead it introduces. This becomes particularly challenging for end-to-end processes, including training and inference, for conventional neural networks on FHE-encrypted data. Additionally, machine learning tasks require a high throughput system due to data-level parallelism. However, existing FHE accelerators only utilize a single SoC, disregarding the importance of scalability. In this work, we address these challenges through two key innovations. First, at an algorithmic level, we combine hyperdimensional Computing (HDC) with FHE. The machine learning formulation based on HDC, a brain-inspired model, provides lightweight operations that are inherently well-suited for FHE computation. Consequently, FHE-HD has significantly lower complexity while maintaining comparable accuracy to the state-of-the-art. Second, we propose an efficient and scalable FHE system for FHE-based machine learning. The proposed system adopts a novel interconnect network between multiple FHE accelerators, along with an automated scheduling and data allocation framework to optimize throughput and hardware utilization. We evaluate the value of the proposed FHE-HD system on the MNIST dataset and demonstrate that the expected training time is 4.7 times faster compared to state-of-the-art MLP training. Furthermore, our system framework exhibits up to 38.2 times speedup and 13.8 times energy efficiency improvement over the baseline scalable FHE systems that use the conventional data-parallel processing flow.

Homomorphic encryption (HE) is one of the most promising solutions to secure cloud computing. The number theoretic transform (NTT) that is widely used for convolution operations in HE requires a large amount of computation and has high parallelism, and therefore it has been a good candidate for hardware acceleration. Nevertheless, prior NTT hardware solutions for HE-based applications are impractical in most applications because they do not seriously consider the critical bootstrapping procedure that allows unlimited homomorphic operations on encrypted data. In this paper, we suggest practical bootstrappable parameters, specifically for an established residue number system (RNS)based HE scheme, and apply them to our NTT hardware design. In addition, to limit the size of internal memory for roots of unity increased by the bootstrappable parameters, only a few roots of unity are stored and others are generated on the fly. In our NTT hardware architecture, multiple NTT butterfly units (BUs) are efficiently deployed for high throughput and high resource utilization. In particular, several groups of BUs for respective moduli work in a parallel and pipelined manner, which is effective in an RNS-based HE scheme with a number of moduli. Our implementation on a Xilinx UltraScale FPGA with the bootstrappable parameters achieves a $118 \times$ faster processing speed than a software implementation, and it further provides various trade-off choices such as the number of DSP slices against BRAMs based on available FPGA resources.

This paper proposes a user selection algorithm in space division multiple access (SDMA) systems. In the proposed algorithm, each user calculates signal to interference plus noise ratio (SINR) taking angle-of-departure, angle-of-arrival, and channel environment with clustering nature into consideration. The base station selects several users who have the high SINR to increase the system throughput. The simulation results show that the proposed algorithm achieves higher throughput compare to the conventional algorithm in SDMA systems.