This paper introduces Cellular-X, an LLM-powered agent designed to automate cellular base station (BS) maintenance. Leveraging multimodal LLM and retrieval-augmented generation (RAG) techniques, Cellular-X significantly enhances field engineer efficiency by quickly interpreting user intents, retrieving relevant technical information, and configuring a BS through iterative self-correction. Key features of the demo include automatic customized BS setup, document-based query answering, and voice-controlled configuration reporting and revision. We implemented Cellular-X on a USRP X310 testbed for demonstration. Demo videos and implementation details are available at https://github.com/SeaBreezing/Cellular-X.

Visual question answering (VQA) plays a vital role in advancing surgical education. However, due to the privacy concern of patient data, training VQA model with previously used data becomes restricted, making it necessary to use the exemplar-free continual learning (CL) approach. Previous CL studies in the surgical field neglected two critical issues: i) significant domain shifts caused by the wide range of surgical procedures collected from various sources, and ii) the data imbalance problem caused by the unequal occurrence of medical instruments or surgical procedures. This paper addresses these challenges with a multimodal large language model (LLM) and an adaptive weight assignment strategy. First, we developed a novel LLM-assisted multi-teacher CL framework (named LMT++), which could harness the strength of a multimodal LLM as a supplementary teacher. The LLM's strong generalization ability, as well as its good understanding of the surgical domain, help to address the knowledge gap arising from domain shifts and data imbalances. To incorporate the LLM in our CL framework, we further proposed an innovative approach to process the training data, which involves the conversion of complex LLM embeddings into logits value used within our CL training framework. Moreover, we design an adaptive weight assignment approach that balances the generalization ability of the LLM and the domain expertise of conventional VQA models obtained in previous model training processes within the CL framework. Finally, we created a new surgical VQA dataset for model evaluation. Comprehensive experimental findings on these datasets show that our approach surpasses state-of-the-art CL methods.

Visual question answering (VQA) can be fundamentally crucial for promoting robotic-assisted surgical education. In practice, the needs of trainees are constantly evolving, such as learning more surgical types and adapting to new surgical instruments/techniques. Therefore, continually updating the VQA system by a sequential data stream from multiple resources is demanded in robotic surgery to address new tasks. In surgical scenarios, the privacy issue of patient data often restricts the availability of old data when updating the model, necessitating an exemplar-free continual learning (CL) setup. However, prior studies overlooked two vital problems of the surgical domain: i) large domain shifts from diverse surgical operations collected from multiple departments or clinical centers, and ii) severe data imbalance arising from the uneven presence of surgical instruments or activities during surgical procedures. This paper proposes to address these two problems with a multimodal large language model (LLM) and an adaptive weight assignment methodology. We first develop a new multi-teacher CL framework that leverages a multimodal LLM as the additional teacher. The strong generalization ability of the LLM can bridge the knowledge gap when domain shifts and data imbalances occur. We then put forth a novel data processing method that transforms complex LLM embeddings into logits compatible with our CL framework. We also design an adaptive weight assignment approach that balances the generalization ability of the LLM and the domain expertise of the old CL model. Finally, we construct a new dataset for surgical VQA tasks. Extensive experimental results demonstrate the superiority of our method to other advanced CL models.

This paper investigates the potential of large language models (LLMs) in accelerating the development of complex signal-processing algorithms on field-programmable gate arrays (FPGAs) for software-defined radio (SDR) systems. Using the Fast Fourier Transform (FFT) algorithm as a case study, we identify two common challenges in applying LLMs to realize intricate wireless communication algorithms on FPGA: 1) handling convoluted mathematical problems and 2) scheduling the execution of sub-modules within the hardware structure. To overcome the first problem, we adapt the chain-of-thought (CoT) prompting technique with a length-limit strategy to enhance the LLM’s Verilog writing performance. To handle the second problem, we develop a novel iterative in-context learning (IICL) prompting scheme that utilizes the iterative structure within the FFT module to perform in-context learning (ICL). These efforts significantly reduce the LLM’s error rate in completing the FFT implementation task and make possible the successful generation of a 64-point FFT module in Verilog, marking a significant milestone as the first LLM-written complex signal-processing algorithm for wireless communication on FPGA.

Signal clipping is one of the most efficient signal peak-to-average power ratio (PAPR) reduction schemes for MIMO-OFDM receivers. It significantly reduces the nonlinear distortion caused by the power amplifiers (PAs) in the transmitter but introduces clipping distortion at the same time. Although the optimization of PA nonlinear distortion or clipping distortion has been well studied in previous research, the joint system optimization with both the nonlinear distortion from the transmitter and clipping distortion from the receiver is still a blank due to the complex PA modeling. This paper simplified the PA model with inter-modulation product (IMP) analysis and derived the expression of the symbol error rate (SER) in nonlinear clipped MIMO-OFDM systems. Based on the derivation, we found the optimal system setting to reach SER lower bound when PA nonlinearity and clipping distortion are simultaneously considered. The functions and methodologies in this paper can be a useful reference for system-level optimization for clipped MIMO-OFDM systems.

Interleaved Frequency Division Multiple Access (IFDMA) has the salient advantage of lower Peak-to-Average Power Ratio (PAPR) than its competitors like Orthogonal FDMA (OFDMA). A recent research effort of ours put forth a new IFDMA transceiver design significantly less complex than conventional IFDMA transceivers. The new IFDMA transceiver design reduces the complexity by exploiting a certain correspondence between the IFDMA signal processing and the Cooley-Tukey IFFT/FFT algorithmic structure so that IFDMA streams can be inserted/extracted at different stages of an IFFT/FFT module according to the sizes of the streams. Although our prior work has laid down the theoretical foundation for the new IFDMA transceiver’s structure, the practical realization of the transceiver on specific hardware with resource constraints has not been carefully investigated. This paper is an attempt to fill the gap. Specifically, this paper puts forth a heuristic algorithm called multi-priority scheduling (MPS) to schedule the execution of the butterfly computations in the IFDMA transceiver with the constraint of a limited number of hardware processors. The resulting FFT computation, referred to as MPS-FFT, has a much lower computation time than conventional FFT computation when applied to the IFDMA signal processing. Importantly, we derive a lower bound for the optimal IFDMA FFT computation time to benchmark MPS-FFT. Our experimental results indicate that when the number of hardware processors is a power of two: 1) MPS-FFT has near-optimal computation time; 2) MPS-FFT incurs less than 44.13% of the computation time of the conventional pipelined FFT.

Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics. There is a tradeoff between the controller’s sampling preference and the balancing of loads among devices. On the one hand, the controller may prefer to sample some of the IoT devices on the flow path because they yield more accurate flow statistics. On the other hand, it is desirable to sample the devices uniformly so that their energy consumptions and lifespan are balanced. This paper formulates the flow sampling problem in large-scale SDIoT networks by means of a Markov decision process and devises policies that strike a good balance between these two goals. Three classes of policies are investigated: the optimal policy, the state-independent policies, and the index policies (including the Whittle index and a second-order index policies). The second-order index policy is the most desired policy among all: 1) in terms of performance, it is on an equal footing with the Whittle index policy, and outperforms the state-independent policies by much; 2) in terms of complexity, it is much simpler than the optimal policy, and is comparable to state-independent policies and the Whittle index policy; 3) in terms of realizability, it requires no prior information on the network dynamics, hence is much easier to implement in practice.