Generative AI, a cutting-edge technology, involves artificial intelligence models capable of producing new data or contents based on task inputs. The Principles of Communications, a required pivotal course for aspiring telecommunication engineers, leverages communication system experiments to bridge theoretical concepts with real-world applications. This paper delves into the utilization of Generative AI in designing communication system experiments from both the teacher's and students' perspectives. Specifically, we explore how teachers can employ Generative AI to craft experiment prompts and guide ChatGPT to generate desired lab results. The methodologies outlined in this paper are not limited to telecommunication courses but can be extrapolated to other experimental disciplines, offering a valuable resource for teachers and students.

This paper reports a reform scheme for project-based courses of an engineering-practicing-oriented special class in our university, aiming at engaging undergraduate students in research and development via a project-based teaching/learning model. To address a number of key challenges in effective curriculum design, fair and comprehensive evaluation and stimulating students' innovative thinking for the current courses, we are working on formulating a two-year pipeline of progressive project-based courses, redesigning the course curriculum with novel contents, establishing a process-oriented and multi-dimensional course grading scheme, and implementing proposal-midterm-final defense stages.

Ovarian cancer remains a significant global health challenge and its survival prognosis plays an important role in clinic practice. In this paper, we design a broad learning model for survival prognosis (SPBL) to enhance the accuracy and ob-jectivity of survival predictions for ovarian cancer patients using machine learning techniques. Prognoses often rely exclusively on lesion information; however, our SPBL model integrates multi-source data. We also apply an interpretable SHAP model, to provide transparent and understandable explanations for the 3-year survival prognosis generated by the SPBL model. Based on the SEER database, we evaluate the propose models. The SPBL model exhibited notable improvements in performance, achieving increases in AUC, F1 score, and accuracy by 1.34%, 7.51%, and 6.08% compared with Random Forest, AdaBoost, GBDT, and XGBoost. The significance analysis suggest that our model can significantly enhance the prognosis process, leading to better-informed clinical decisions and optimized treatment strategies.

With the rapid advancement of 5G networks, data-intensive services has been surging and significantly increasing energy consumption. It is a critical challenge in addressing energy efficiency for 5G access networks. In this paper, we propose an effective load balancing algorithm that optimizes energy efficiency as users access multiple services from base stations (BS). Utilizing network slicing, the physical network is segmented into multiple independent virtual networks. We have applied graph neural networks (GNN) to extract features from dynamic traffic, model relationships between different services, and manage resource competition among BSs and network slices, treating each network slice as a distinct node. The GNN model predicts nodes workload, guiding the allocation of frequency domain resources and facilitating load balancing. Additionally, to reduce operational times and minimize disparities among slices at each BS, we propose a deep reinforcement learning (DRL) strategy. This strategy is integrated with the C-states mechanism of CPUs on general x86 computing platforms to further enhance energy efficiency. In simulations, our GNN algorithm demonstrated high precision in predicting and allocating frequency domain resources, achieving a high accuracy of 99.8%. Our DRL algorithm showed a maximum global energy efficiency improvement of 12% compared to benchmark algorithms. Overall, our approach enhanced energy efficiency by an average of 3.155 B/J under conditions of resource competition, significantly outperforming strategies that allocate equal physical resource blocks (PRB).

With the rapid development of the wireless communication technologies, it has become a challenging problem for engineering education for senior students on telecommunication major to catch up with the latest development of technologies. In this paper, a simplified 5G experiment system is established online, which is a blended lab platform including virtual simulation and online real-world experiment. Using the platform, the student can study the 5G system modules in the front of his computer and program the 5G module by software-defined radio technology.

Massive MIMO tremendously improves system capacity in the fifth generation (5G) wireless network with spacial diversity. Multi-user Multi-Input Multi-Output (MU-MIMO) user selection and resource allocation becomes more complex and challenging with a significantly increasing number of antenna in 5G network. In this paper, we aim to identify the user channel correlation and select users in the MU-MIMO system via the deep deterministic policy gradient (DDPG). Considering a short timing budget for real-time scheduling, we innovatively tailor DDPG into a short latency decision making process by asynchronously training update. By modeling MU-MIMO scheduling into a markov decision process (MDP), the proposed real-time DDPG (RT-DDPG) algorithm can be well-trained and outputs the selected users with their frequency domain resource simultaneously on all subbands, satisfying the minimum correlation among selected users. Numerical results validate the proposed RTDDPG user scheduling algorithm scheme drops 56% of latency comparing with traditional DDPG and significantly outperforms the traditional MU-MIMO user scheduling algorithms in system throughput.

With recent advances in data-as-a-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data.

With recent advances in data-as-a-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data. As many aggregate queries are composed of multiset operations such as set union and subset, we first develop a family of privacy-preserving authentication protocols for primitive multiset operations. Using these protocols as building blocks, we present a privacy-preserving authentication framework for various aggregate queries and further optimize their authentication performance. Security analysis and empirical evaluation show that our proposed privacy-preserving authentication techniques are feasible and robust under a wide range of system workloads.

WiFi has become one of the major network access networks due to its simple technical implementation and high‐bandwidth provisioning. In this paper, we studied software defined WiFi networks (SDWN) against traditional WiFi networks to understand the potential benefits, such as the ability of SDWN to effectively hide the handover delay between access points (AP) of the adoption of the SDWN architecture on WiFi networks and identify representative application scenarios where such SDWN approach could bring additional benefits. This study delineated the performance bottlenecks such as the throughput degradation by around 50% compared with the conventional WiFi networks. In addition, our study also shed some insights into performance optimization issues. All of the performance measurements were conducted on a network testbed consisting of a single basic service set (BSS) and an extended service set (ESS) managed by a single SDN controller deployed with various laboratory settings. Our evaluation included the throughput performance under different traffic loads with different number of nodes and packet sizes for both TCP and UDP traffic flows. Handover delays were measured during the roaming phase between different APs against the traditional WiFi networks. Our results have demonstrated the tradeoff between performance and programmability of software defined APs.

For IEEE 802.11 networks with multiple basic service sets (BSSs), most studies have focused on how to allocate different frequency sub-channels to BSSs for minimizing the co-channel interference. With the significant increase of the sub-channel bandwidth, however, it becomes increasingly important to study the network performance with universal frequency reuse. In this paper, we focus on an uplink M-BSS IEEE 802.11 network, where all the BSSs share the frequency band rather than operate at different sub-channels. By dividing the nodes in each BSS into multiple groups according to the set of access points (APs) they can be heard by, the steady-state points of M BSSs in saturated conditions are obtained as the functions of the number of nodes in each group and the initial backoff window size of nodes of each BSS. The maximum network throughput is further characterized by optimally choosing the initial backoff window sizes of all the nodes and shown to be closely dependent on the percentage of nodes that can be heard by multiple APs. The comparison with orthogonal frequency division reveals that although the maximum network throughput is degraded due to interference among BSSs, a higher network data rate can still be achieved by universal frequency reuse, which makes it a preferable option for multi-BSS IEEE 802.11 networks.

Due to the rapid growth of real-time applications in wireless local area networks (WLANs), quality-of-service (QoS) guarantee becomes one of the key issues for IEEE 802.11e enhanced distributed channel access (EDCA) networks. In contrast to most existing studies which only focus on providing delay guarantee to real-time flows, in this paper we study the open question of how to adaptively tune system parameters to maximize the aggregate throughput of non-real-time flows with a certain mean access delay constraint on real-time flows for saturated IEEE 802.11e EDCA networks. Explicit expressions of the maximum aggregate throughput of non-real-time flows and the optimal initial backoff window sizes are obtained, and verified by simulation results. The analysis shows that for a given mean access delay constraint, the maximum aggregate throughput of non-real-time flows declines as the number of real-time nodes grows. It drops to zero when the number of realtime nodes exceeds a critical threshold, indicating that the delay requirement cannot be satisfied. An admission control scheme is further proposed, where the maximum number of real-time nodes that can be enrolled is derived as a linearly increasing function of the mean access delay constraint.

Cloud gaming has emerged as a promising approach to more affordable and accessible games. By rendering high-end video games in the cloud and streaming encoded game scenes to players via the broadband networks, users are relieved from downloading or installing game software. With cloud gaming, users can easily play high-end 3-D video games on any devices anytime and anywhere. In this paper, we conducted a comprehensive measurement study of a leading cloud gaming system in China, namely, CloudUnion. Unlike the previous work, our measurement study was based on an in-depth understanding of the internal mechanisms of CloudUnion, and thus we were able to reveal problems that cannot be observed in a black-box approach. We built a dedicated measurement platform, which enables us to study CloudUnion from different views, including the global view, local view, and user view. We also conducted a comparison study with another cloud gaming system, namely, GamingAnywhere. Our measurement results unveil the pros and cons of the current cloud gaming system design, and bring forth important insights about the cloud infrastructure, user behaviors, traffic patterns, user-perceived quality, and so on. Our work will be valuable for the design of future cloud gaming systems.

The hybrid architecture of content distribution network (CDN) and peer to peer (P2P) is promising in providing online streaming media services. In this paper, we conducted a comprehensive measurement study on Kankan, one of the leading VoD streaming service providers in China that is based on a hybrid CDN-P2P architecture. Our measurements are multi-fold, as follows. 1) Kankan adopts a loosely-coupled hybrid architecture , in which the user requests are handled by its CDN and P2P network independently. 2) Kankan deploys a small-scale CDN densely in three geographic clusters in China. It adopts specific redirection servers to dispatch the nationwide requests. 3) Kankan adopts a dual-server mechanism to enhance start-up video streaming. It also provides the CDN acceleration in case of inefficient P2P streaming performance. 4) According to our studies on the peer cache lists, the video contents stored in Kankan peers update quite slowly. The average lifetime of cached videos is longer than one week. Our results show that, by utilizing the slow-varying contents cached in peers and deploying various CDN enhancement mechanisms , Kankan provides a large-scale VoD streaming service with a small-scale fixed infrastructure. Insights obtained in this study will be valuable for the development and deployment of future hybrid CDN-P2P VoD streaming systems.

Overlay networks are resilient in transferring data through intermediate nodes. The dynamic stochastic shortest path (DSSP) can be utilized in overlay networks to find the optimized relay paths; however, DSSP depends on the link delay properties/states (i.e., delay distribution and delay average range). Nevertheless, it is difficult to acquire accurate link delay states to approximate the link characteristics due to possible measurement errors. In this paper, we first proposed a convenient DSSP estimation approach to approximate the link stochastic delay considering the tradeoff between the immediate delay and historical delay samples in stochastic overlay networks. Then, in order to evaluate the performance of DSSP, we conducted a comprehensive simulation study to compare DSSP with the shortest path computed using classic routing algorithms with the average delay values and delay errors due to the variation of delay distributions and updating intervals. The experiment results show that the proposed DSSP delay estimation method is more reliable and outperforms the conventional shortest path routing with the delay estimation using average delay and delay errors. In addition, we also proposed a refined heuristic K-shortest stochastic path routing algorithm using the proposed delay estimation method. In two typical overlay relay network scenarios, the simulation results show that the proposed stochastic routing algorithm outperforms the classic routing algorithms in reducing the average delay by 20% – 40% and the packet loss for nearly 50%.

For Internet video services, the high fluctuation of user demands in geographically distributed regions results in low resource utilizations of traditional content distribution network systems. Due to the capability of rapid and elastic resource provisioning, cloud computing emerges as a new paradigm to reshape the model of video distribution over the Internet, in which resources (such as bandwidth, storage) can be rented on demand from cloud data centers to meet volatile user demands. However, it is challenging for a video service provider (VSP) to optimally deploy its distribution infrastructure over multiple geo-distributed cloud data centers. A VSP needs to minimize the operational cost induced by the rentals of cloud resources without sacrificing user experience in all regions. The geographical diversity of cloud resource prices further makes the problem complicated. In this paper, we investigate the optimal deployment problem of cloud-assisted video distribution services and explore the best tradeoff between the operational cost and the user experience. We aim to pave the way for building the next-generation video cloud. Toward this objective, we first formulate the deployment problem into a min-cost network flow problem, which takes both the operational cost and the user experience into account. Then, we apply the Nash bargaining solution to solve the joint optimization problem efficiently and derive the optimal bandwidth provisioning strategy and optimal video placement strategy. In addition, we extend the algorithms to the online case and consider the scenario when peers participate into video distribution. Finally, we conduct extensive simulations to evaluate our algorithms in the realistic settings. Our results show that our proposed algorithms can achieve a good balance among multiple objectives and effectively optimize both operational cost and user experience.

Measurement studies of popular peer-to-peer (P2P) live video systems reveal that there exists extreme unfairness among peers in the swarm. Such kind of unfairness will provide disincentives to altruistic super peers and encourage free riding behavior in the system. It is essential for video service providers to take fairness into consideration when designing their systems. In this paper, we develop a simple model of P2P live video systems to understand the fairness problem from a theoretic perspective. We identify the fundamental tradeoff between fairness and performance, and propose a semidistributed algorithm based on the subgradient method to tune the P2P live video system toward optimal fairness while still maintaining the targeted universal streaming rate. We also conduct extensive trace-driven simulations to validate the effectiveness of our proposed algorithm. The simulation results show that our algorithm can guide the system toward optimal fairness quickly without degrading streaming performance at the same time.

Massive multi-player online games (MMOGs) have been attracting thousands of millions of participants on the Internet in the past decades. The newly emerging smart phones and game consoles together with PCs have enlarged the player base to an even larger scale. The increasing game traffic may generate significant real-time traffic across different ISP networks. In this paper, we conducted a measurement study of the traffic locality property of a popular online game, Call-of-Duty (CoD), which is a hybrid peer-to-peer (P2P) client/server massive multi-player online first person shooter (MMOFPS) game in the play station network (PSN) over the Internet. To facility our measurement, we designed and implemented a peer crawler over the PSN. Our instrumented crawler applies the principle of the ARP poisoning attack in our justified scenario so that our crawler is able to penetrate the PSN to harvest player's information successfully. We analyzed the measurement results for finer granularity at the autonomous system (AS) level compared with previous measurement studies. Our results show that the sessions in this CoD game are constructed with players' locality in mind. Nevertheless, optimized locality-aware game sessions are yet to be found. Insights obtained from this study may be valuable for the development and deployment of future P2P online gaming systems.

Many Internet applications are designed and deployed as overlay applications. The potential mismatch between the application overlay and the network underlay has driven the demand for designing locality-aware applications in order to reduce emerging huge inter-domain traffic load. In this paper, we study a fundamental problem of measuring AS paths between two Internet hosts using three methods including traceroute-based direct measurement, BGP-based indirect inference and graph-based shortest AS path. We conducted a measurement study of AS paths to evaluated the accuracy and complexity of the above three AS path measurement methods and the corresponding tools. Inspired from our experiment results, we proposed a hybrid progressive method to combine the traceroute probes and the BGP tables to enhance the IP-to-AS mapping process to achieve a more accurate estimation of AS paths. We also found that the missing IP addresses in the traceroute measurement decrease the accuracy of the traceroute-based method; however, this performance degradation can be compensated using BGP tables. Our study leads a more accurate IP-to-AS mapping tool and it can provide a solid support for locality-aware Internet applications.

We study a natural and potentially devastating attack against BitTorrent, namely, attacking the initial seed in a torrent's early stages. The goal of this attack is to diminish the seed's ability to upload blocks. If the attacker can discover and react quickly enough to the new torrent, it can possibly "nip the torrent in its bud," preventing all of the leechers from obtaining the entire file. We consider two natural seed attacks: the bandwidth attack and the connection attack. We take a three-prong approach to analyze these attacks. First, we actually launch and measure the attacks using popular BitTorrent seeds (Azureus, uTorrent, and BitTornado). To this end, because we do not want to interfere with torrents in the wild, we have created our own private torrents within PlanetLab. Second, to gain insight into our empirical results, we carefully analyze the connection management and seeding algorithms in open-source BitTorrent seeds. Third, we construct a simple fluid model which provides additional insights into the empirical results. We have discovered that the three BitTorrent seeds investigated are quite resilient to such an attack. The observations and conclusions in this paper can help P2P developers design highly-resilient P2P systems.

Peer Exchange (PEX), in which peers directly exchange with each other lists of active peers in the torrent, has been widely implemented in modern BitTorrent clients for decentralized peer discovery. However, there is little knowledge about the behavior of PEX in operational systems. In this paper, we perform both passive measurements and Planetlab experiments to study the impact and properties of BitTorrent PEX. We first study the impact of PEX on the download efficiency of BitTorrent. We observe that PEX can significantly reduce the download time for some torrents. We then analyze the freshness, redundancy and spread speed of PEX messages. Finally, we also conduct large- scale Planetlab experiments to understand the impact of PEX on the overlay properties of BitTorrent.

P2P live streaming systems are developed in two major approaches: tree-push versus mesh-pull. The hybrid push-pull streaming, as an emerging and promising approach, offers a good tradeoff between traffic overhead and system throughput. In this paper, we demonstrate that video redundancy is a large c

Peer-to-peer overlays can be used for service discovery over a global network fabric. We describe and evaluate a new service indexing mechanism for in-band streaming services such as application relays, mixers, and media transcoders. For this type of service, the location of the service in the network and service admission status are key attributes. We describe and analyze a service indexing mechanism which uses network position-based advertisement. We show that this mechanism gives good service selection, provides a close to uniform advertisement distribution in the overlay, reduces message overhead, and exhibits acceptable stability and setup delay.

This paper explores how to remotely monitor network-wide quality in mesh-pull P2P live streaming systems. Peers in such systems advertise to each other buffer maps which summarize the chunks of the video stream that they currently have cached and make available for sharing. We demonstrate how buffer maps can be exploited to monitor network-wide quality. We show that the information provided in a peer's advertised buffer map correlates with that peer's viewing-continuity and startup latency. Given this correlation, we remotely harvest buffer maps from many peers and then process these buffer maps to estimate the video playback quality. We apply this methodology to a popular P2P live streaming system, namely, PPLive. To harvest buffer maps, we build a buffer-map crawler and also deploy passive sniffing nodes. We process the harvested buffer maps and present results for network-wide playback continuity, startup latency, playback lags among peers, and chunk propagation patterns. The results show that this methodology can provide reasonably accurate estimates of ongoing video playback quality throughout the network.

An emerging Internet application, IPTV, has the potential to flood Internet access and backbone ISPs with massive amounts of new traffic. Although many architectures are possible for IPTV video distribution, several mesh-pull P2P architectures have been successfully deployed on the Internet. In order to gain insights into mesh-pull P2P IPTV systems and the traffic loads they place on ISPs, we have undertaken an in-depth measurement study of one of the most popular IPTV systems, namely, PPLive. We have developed a dedicated PPLive crawler, which enables us to study the global characteristics of the mesh-pull PPLive system. We have also collected extensive packet traces for various different measurement scenarios, including both campus access networks and residential access networks. The measurement results obtained through these platforms bring important insights into P2P IPTV systems. Specifically, our results show the following. 1) P2P IPTV users have the similar viewing behaviors as regular TV users. 2) During its session, a peer exchanges video data dynamically with a large number of peers. 3) A small set of super peers act as video proxy and contribute significantly to video data uploading. 4) Users in the measured P2P IPTV system still suffer from long start-up delays and playback lags, ranging from several seconds to a couple of minutes. Insights obtained in this study will be valuable for the development and deployment of future P2P IPTV systems.

Network Address Translation (NAT) commonly prevents nodes without globally valid IP addresses from establishing direct Internet paths. In peer-to-peer networks, peers may utilize intermediate nodes as relays for this NAT traversal. We develop a stochastic relay routing algorithm for selecting appropriate relay nodes. The proposed relay routing algorithm is constructed in a stochastic programming framework by leveraging the actual delay of local links and the statistical delay distributions of non-local overlay links. Single or multiple paths are established via relays between two peers for achieving packet delivery with low delay and small loss. The simulation results showed that the proposed stochastic single/multi-path routing algorithm achieved a much lower packet delay than deterministic shortest path algorithms, which utilize average link delays. We demonstrated the effectiveness of the path diversity provided by our algorithm in reducing packet loss significantly via simulations. Our algorithm is fully distributed and requires only accurate local information. The employment of our algorithm is beneficial for provisioning real-time streaming applications in peer-to-peer networks.

End-to-end available bandwidth estimation is important in understanding network congestion and enhancing service quality. In this paper, we investigate a light-weight probing method for available bandwidth measurement in a queueing analysis approach. Unlike the self-congestion based measurement approach, a light-weight probing technique infers the available bandwidth along a path without congesting the routers along the path. Of particular interest in our investigations, is the squared coefficient of variation (SCV) of the inter-departure process of a periodic probing stream. We analyze approximately the departure process of this probing stream. Simulation results indicate that the proposed hybrid approximation can provide good estimates of the SCV of the probing stream regardless of the stochastic behavior of the arrival process of the cross traffic. Given a measured SCV, inverting this approximation infers the load of the cross traffic on the congested link.

In this paper, we investigated a non-intrusive probing methodology for available bandwidth measurement based on the analysis of the departure process of an active Poisson probing stream. Unlike the self-congestion based available bandwidth measurement, non intrusive techniques are meant to infer the available bandwidth along a path without congesting the path. We propose to probe the end-to-end path using small size packets with exponentially distributed time between consecutive probing packets. Of particular interest to our investigations, is the squared coefficient of variation (SCV) of the inter-departure process of the probing stream. The Internet is modelled as single server queue with two concurrent streams, the probing traffic stream and the cross traffic, we rely on the results on M/sub 1/ + M/sub 2//GI/sub i//1 queueing system and a heavy traffic approximation model to analyze the departure process of the probing stream. Thus, in a real measurement system, given the measured SCV of the probing stream, inverting the approximation helps inferring the load of the cross traffic on an end-to-end-path.

Content delivery network (CDN) is an intermediate layer of infrastructure that helps to efficiently deliver the ever increasing multimedia content from content providers to a large community of geographically distributed clients. Content routing is an essential component of CDN architecture. In this paper we propose a hierarchical content routing architecture for large-scale CDN, in which CDN servers perform inter-cluster content routing based on two-level hierarchical overlay network. We analyze the routing overhead and the corresponding CDN performance of different intra-cluster content routing schemes. In particular, we propose a semi-hashing based scheme for intra-cluster content routing and a content-query based scheme for inter-cluster content routing. Through qualitative analysis and simulations we show that the semi-hashing based scheme is scalable (small routing overhead), efficient (high content sharing efficiency), and flexible (adjustable parameters).