Throughput Maximization in Cloud Radio Access Networks using Network Coding

This paper is interested in maximizing the total throughput of cloud radio access networks (CRANs) in which multiple radio remote heads (RRHs) are connected to a central computing unit known as the cloud. The transmit frame of each RRH consists of multiple radio resources blocks (RRBs), and the cloud is responsible for synchronizing these RRBS and scheduling them to users. Unlike previous works that consider allocating each RRB to only a single user at each time instance, this paper proposes to mix the flows of multiple users in each RRB using instantly decodable network coding (IDNC). The proposed scheme is thus designed to jointly schedule the users to different RRBs, choose the encoded file sent in each of them, and the rate at which each of them is transmitted. Hence, the paper maximizes the throughput which is defined as the number of correctly received bits. To jointly fulfill this objective, we design a graph in which each vertex represents a possible user-RRB association, encoded file, and transmission rate. By appropriately choosing the weights of vertices, the scheduling problem is shown to be equivalent to a maximum weight clique problem over the newly introduced graph. Simulation results illustrate the significant gains of the proposed scheme compared to classical coding and uncoded solutions.Comment: 7 pages, 7 figure

Coalition Formation Game for Cooperative Content Delivery in Network Coding Assisted D2D Communications

Device-to-device (D2D) communications have shown a huge potential in cellular offloading and become a potential technology in 5G and beyond. In D2D networks, the requested contents by user devices (UDs) can be delivered via D2D links, thus offloading the content providers (CPs). In this work, we address the problem of minimizing the delay of delivering content in a decentralized and partially D2D connected network using network coding (NC) and cooperation among the UDs. The proposed optimization framework considers UDs’ acquired and missing contents, their limited coverage zones, NC, and content’s erasure probability. As such, the completion time for delivering all missing contents to all UDs is minimized. The problem is modeled as a coalition game with cooperative-players wherein the payoff function is derived so that increasing individual payoff results in the desired cooperative behavior. Given the intractability of the formulation, the coalition game is relaxed to a coalition formation game (CFG). A distributed coalition formation algorithm relying on merge-and-split rules is developed for solving the relaxed problem at each transmission. The effectiveness of the proposed solution is validated through computer simulation against existing schemes

Coalition Formation Game for Cooperative Content Delivery in Network Coding Assisted D2D Communications

Device-to-device (D2D) communications have shown a huge potential in cellular offloading and become a potential technology in 5G and beyond. In D2D networks, the requested contents by user devices (UDs) can be delivered via D2D links, thus offloading the content providers (CPs). In this work, we address the problem of minimizing the delay of delivering content in a decentralized and partially D2D connected network using network coding (NC) and cooperation among the UDs. The proposed optimization framework considers UDs’ acquired and missing contents, their limited coverage zones, NC, and content’s erasure probability. As such, the completion time for delivering all missing contents to all UDs is minimized. The problem is modeled as a coalition game with cooperative-players wherein the payoff function is derived so that increasing individual payoff results in the desired cooperative behavior. Given the intractability of the formulation, the coalition game is relaxed to a coalition formation game (CFG). A distributed coalition formation algorithm relying on merge-and-split rules is developed for solving the relaxed problem at each transmission. The effectiveness of the proposed solution is validated through computer simulation against existing schemes

Low-Complexity Power Allocation for Network-Coded User Scheduling in Fog-RANs

Consider a Fog Radio Access Network (FRAN) in which a cloud base station (CBS) is responsible for scheduling user-equipments (UEs) to a set of radio resource blocks (RRBs) of Fog Access Points (F-APs) and for allocating power to the RRBs. The conventional graphical approach for solving the coordinated scheduling and power control problem in FRAN requires prohibitive computational complexity. This letter, instead, proposes a low-complexity solution to the problem under the constraint that all the scheduled UEs can decode the requested files sent by their associated RRBs/F-APs. Unlike previous solution that requires constructing the total power control graph, the proposed computationally efficient solution is developed using a single power control subgraph. Numerical results reveal a close-to-optimal performance of the proposed method in terms of throughput maximization for correlated channels with a significant reduction in the computational complexity

Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering

Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review could improve the program repair further. Large language models, trained with Natural Language (NL) and Programming Language (PL), can contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of PL and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both PL and NL, on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperformed each of the previous models. With the advent of code generative models like Codex and GPT-3.5-Turbo, we also performed zero-shot and few-shots learning-based prompt engineering to assess their performance on these datasets. However, the practical application of using LLMs in the context of automated program repair is still a long way off based on our manual analysis of the generated repaired codes by the learning models.Comment: 12 pages, 2 figures, 4 table