Large language models (LLMs), such as ChatGPT, have demonstrated remarkable capability in question answering but face challenges when it comes to knowledge-based rea-soning, such as limited training data and hallucination. To address these challenges, integrating LLMs with knowledge graphs (KGs) has emerged as a promising solution. However, the cost associated with training and inference of LLMs is high. Our method integrates the Retrieval-Augmented Generation (RAG) paradigm, incorporating relevant information from KGs alongside the question to enhance LLMs' reasoning process without training. Moreover, we propose a novel concept of self-knowledge motivation to reduce the overhead of inference, which prompts LLMs to integrate retrieved information with their internal knowledge for reasoning before seeking additional queries to KGs. Experimental results showcase improvements in answer accuracy and a reduction in LLMs' API calls compared to the latest published state-of-the-art (SOTA) method employing an identical paradigm, underscoring the efficiency and effectiveness of our method.

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different resolutions and encoded at different bitrates. One key issue in omnidirectional video streaming is how to choose the optimal representations for each tile at the server to save the overall transmission bitrate to all users while offering them satisfactory quality. This is different from the adaptive bitrate-based method that optimizes the downloading procedure of individual users, where the given video representations are stored on the server. In this work, we focus on optimization for the encoding of omnidirectional video streaming by using the optimal combination of tile representations. To achieve our goal, we formulate the selection of the representations into an optimization problem in which the transmission bitrate of all the representations is minimized with a quality constraint. By using this constraint, we can improve the average quality of omnidirectional videos for users. More specifically, we first construct the tile-level rate-distortion (R-D) model and determine the available tile bandwidth based on the previous viewers’ statistics. Then, we formulate the representation selection problem based on the obtained R-D model and tile bandwidth. Finally, we solve this problem to obtain the optimal combination of tile representations so that we can transmit the omnidirectional video to users with satisfactory quality but low bitrate. The experimental results demonstrate the effectiveness of our proposed approach when it is applied to omnidirectional video streaming.

The non-orthogonal multiple access (NOMA) technique has demonstrated potential for the multicast of multiple videos. However, it simply multiplexes limited number of signals in each single channel and cannot satisfy different video quality requirements of multiple users. To resolve this problem, we construct the NOMA-based uncoded multi-user video transmission (NOMA-UMVT) system in which the allocation of power and channel resources to all the users is jointly optimized to guarantee high video quality. Specifically, we first implement the power allocation of multiuser by converting it into the inter-channel and intra-channel allocation sub-problems. After solving these sub-problems for power allocation, we assign channels with the proposed two-staged channel assignment algorithm. The simulation results demonstrate the superior performance of our proposed NOMA-UMVT system when it is applied to transmit videos to multiple users.

Given the scarcity of wireless resources, demand-side management for cellular networks is indispensable. In this paper, we investigate base station (BS) level dynamic pricing in the shared access C-RAN, such that the overall demand-supply equilibrium can be achieved. The main challenge is how to promptly and accurately determine the price for utilizing the resources of each BS, without knowing how users will behave when price changes. To this end, we adopt the deep reinforcement learning (DRL) method, which can learn from the environment and make decisions accordingly. We propose a domain-specific DRL-based dynamic pricing scheme and elaborately design the state, action and reward, as well as utilize other techniques for implementation of the dueling double deep Q-network (D3QN) algorithm. Simulations are conducted to test the trained network, and the results demonstrate the stability and performance improvement over baseline methods.

Multiuser video transmission, where the server transmits videos to multiple users that require different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and the simulation results show that the proposed method can achieve the best system performance.

Multiuser video transmission, where the server transmits videos to multiple users that request different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate the system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and simulation results show that the proposed method can achieve the best system performance.

Non-orthogonal multiple access (NOMA) has been considered as a significant candidate technique for the next generation wireless communication to support high throughput and massive connectivity. It allows different users to be multiplexed on one channel through applying superposition coding at the transmitter and successive interference cancellation (SIC) at the receiver. To fully utilize the benefit of the NOMA technique, the key problem is how to optimally allocate resources, such as power and channels, to users to maximize the system performance. There have been some existing works on the power allocation for the single-carrier NOMA system. However, how to optimally assign channels in the multi-carrier NOMA system is still unclear. In this paper, we propose a deep reinforcement learning framework to allocate resources to users in a near optimal way. Specifically, we exploit an attention-based neural network (ANN) to perform the channel assignment. Simulation results show that the proposed framework can achieve better system performance, compared with the state-of-the-art approaches.

Uncoded linear video transmission has recently attracted people's attention due to its capacity to provide robust and scalable transmission. However, in reality, with the fluctuation of the wireless channels, the received quality may not be good enough. In such a case, the data may need to be transmitted multiple times to exploit both the time and frequency diversities to improve the received quality. Such a problem has never been investigated in the literature of uncoded video transmission. To resolve the problem, in this paper, we propose a framework, named MCast, to utilize the time and frequency diversities to achieve high-quality linear video transmission. We study how to optimally allocate the power and assign the channels at each time slot to the source data such that the overall performance is maximized. Specifically, we first derive a closed-form optimal power allocation solution for any given channel assignment. With the optimal power allocation, we then propose a suboptimal channel assignment scheme, where we sort the channels with their gains and assign the channels one-by-one to the corresponding block that can reduce the most reconstruction error. Finally, we compare MCast system with four other systems that are based on Softcast and Parcast, and simulation results show that MCast system can achieve better performance in terms of both PSNR performance and visual quality.