360-degree videos have gained considerable popularity by offering immersive experiences to viewers. However, they consume a significant amount of bandwidth and demand specialized caching schemes at the edge network. Previous caching schemes have certain limitations due to either complete historical data or neglect the viewer-varying characteristics in 360-degree videos. In this paper, we address these limitations by proposing online caching schemes for two scenarios: (1) single-video and (2) multi-video. In the single-video scenario, we propose the online Single-Video caching scheme (SV-caching scheme). The SV-caching scheme predicts tile popularity using the PopPred algorithm, and optimizes caching decisions to enhance viewers’ Quality of Experience (QoE) using the CacheOpt algorithm. In the multi-video scenario, we propose the online Multi-Video caching scheme (MV-caching scheme). The MV-caching scheme dynamically allocates cache space for each video using the MVCacheAlloc algorithm according to the video popularity, and optimizes caching decisions using the MVCacheOpt algorithm. We prove that all algorithms achieve sublinear regret, i.e.,O(√K), whereKis the number of viewers. This guarantees that our schemes’ performance approaches the optimal as more viewers are served. Experiments on real-world data demonstrate that our caching schemes outperform existing algorithms in regret, QoE, and hit ratio for both scenarios.

Integrated sensing and communication (ISAC) is an emerging paradigm designed to support next-generation wireless services and applications. However, ISAC systems with limited computation capabilities are unable to handle computation-intensive and latency-sensitive tasks. This paper proposes a novel integrated sensing, communication, and computation (ISCC) network empowered by a reconfigurable intelligent surface (RIS) to mitigate the performance degradation caused by interference between radar sensing and uplink offloading. To effectively coordinate the cross-layer resource allocation among communication, sensing, and computation, we propose a resource scheduling problem. Specifically, we maximize the total computation rate while satisfying the sensing signal-to-noise ratio (SNR) requirement by jointly optimizing the energy allocation for local computing and offloading, the transmit and receive beamforming at the base station (BS), and the RIS reflective beamforming. To address this complex non-convex problem, we develop an efficient scheduling algorithm based on the block coordinate descent (BCD) framework. The iterative algorithm employs the fractional programming algorithm based on Lagrangian dual transform and quadratic transform, the generalized eigenvector methods, the convex relaxation techniques, and the successive convex approximation (SCA) algorithms to solve each subproblem separately. Experimental results demonstrate that the proposed scheme outperforms several baseline methods, confirming that RIS technology can effectively enhance system performance. In addition, we reveal the impact of various parameters on system performance.

Coded caching has emerged as an effective technique to alleviate network congestion. While the individual impact of user cache sizes or file popularity has been investigated independently, their joint impact on coded caching remains unclear. In this paper, we first characterize two information-theoretic lower bounds on the expected transmission rate. The first bound introduces a novel cut-set method that allows users to repeatedly request files at different frequencies, accounting for the heterogeneity of user cache sizes and file popularity. The second bound is derived from the traditional cut-set bound, assuming each file is requested at most once. Then a group-based multi-round decentralized coded caching scheme is introduced. We show that the expected transmission rate for this scheme is at most anO(logK) factor away from the second lower bound, applicable to any cache and popularity distributions, whereKis the number of users. Additionally, by combining with the first lower bound, a tighterO(logKmK/m1) gap between the upper and lower bounds is derived for power function and Zipf cache distributions, wherem1andmKrepresent the minimum and maximum cache sizes, respectively. Simulation results show the superior performance of our proposed scheme.

End-to-end (E2E) semantic communication (SemCom) powered by semantic knowledge base (SKB) is an efficient SemCom framework. However, in practical scenarios, SKB discrepancy among multiple SemCom pairs arises due to dynamic environmental changes (e.g., varying source data or tasks) or system-level alterations (e.g., integration of new SemCom pairs with divergent SKBs). Such discrepancy leads to performance disparity in semantic transmission, where underperforming pairs fail to maintain efficient task execution. To address this challenge, this paper introduces a cooperative SKB update policy, which enables collaborative evolution of SKBs to mitigate SKB discrepancy and improve performance of underperforming pairs. For each SemCom pair endowed with SKB-enabled SemCom, partial local SKB is selected out and uploaded to a mobile edge computing (MEC) server for establishing a global SKB. The global SKB aggregates the advantages of each local SKB, and is broadcasted to all SemCom pairs. Then, each SemCom pair updates their local SKB with the assistance of the global SKB. This process makes the local SKBs more sensible and less ambiguous, thereby enhancing the semantic transmission performance. Furthermore, in order to maximize the cooperative gains under limited uplink budgets of SemCom pairs, a knowledge selection optimization problem is formulated for the selection of the uploaded knowledge. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains, especially for the initially poor-performing pairs, and provide comprehensive performance comparison of the knowledge selection scheme.

The entanglement distribution problem over quantum networks has been widely studied, with the objective of maximizing network throughput, that is, the number of entanglements distributed for all user pairs. However, most of the existing works only focus on throughput maximization while neglecting fairness considerations. In this paper, we first characterize the fairness of an entanglement distribution scheme by introducing a fairness factor based on Element-Wise Inequalities, referred to as EWI-fairness. The EWI-fairness requires that the entanglement distribution rate of each user pair exceeds a certain threshold, ensuring the fair distribution. Second, we enforce fairness guarantees into two existing distribution methods, Temporal Multiplexing Distribution (TMD) and Flow Multiplexing Distribution (FMD). Our theoretical analysis reveals that FMD-based scheme outperforms TMD-based scheme. Therefore, we focus on optimizing FMD-based scheme. Third, we formulate the fair entanglement distribution problem as a linear programming problem, where fairness requirements serve as constraints, aiming to identify the optimal FMD-based scheme with the highest throughput. Simulation results demonstrate that the optimized FMD-based scheme achieves a higher throughput compared to existing schemes under identical fairness requirements.

Coded caching provides an opportunity to reduce data traffic load in peak hours via coded multicast transmissions. This paper studies the coded caching problem for video contents in heterogeneous packet erasure channels. The video contents are encoded in a scalable manner, where each user's decoding quality depends on the number of layers it receives. Two main challenges lie in this problem. (1) In heterogeneous erasure broadcast channels, users may receive varying numbers of packets. Thus, the performance of the system will be limited by the worst channel condition of users, which is referred to as the “worst-user effect” in existing works. (2) In a scenario where users require different video qualities, different numbers of layers may be transmitted to the users, which indicates a reduction in the multicast opportunities. In this paper, we propose a novel coded caching scheme that enables video quality adaptation, addressing these two challenges at once. The insight is to align the number of layers each user requests with its channel condition, ensuring that the amount of data each user attempts to receive is the same, thus maximizing the multicast opportunities. Analyses show that the transmission rate is determined by the average erasure probability among users, thus alleviating the worst-user effect. Simulation results illustrate the superior performance of the proposed scheme compared to state-of-the-art methods.

Coded caching has emerged as a promising technique to alleviate traffic congestion by strategically creating coded multicasting opportunities, even for caches with different demands. For a two-layer cache-aided hierarchical network consisting of a central server, multiple helpers, and multiple users, prior works have characterized the fundamental performance limits of coded caching for this system with the constraint of a uniform user distribution (i.e., each helper serves an equal number of users). However, when the heterogeneity of user distribution is taken into account, there remain open questions. In this article, we consider a two-layer cache-aided hierarchical network with arbitrary user distributions, where a central server is connected via an error-free link to multiple helpers and each user can randomly access one helper. We introduce a new decentralized coded caching scheme and employ the cut-set technique to characterize lower bounds. Our results show that the gap between the upper and lower bound of the achievable rate from server to helpers is within a constant multiplicative (i.e., [1/32]) and additive (i.e., 2) factor, outperforming prior works under uniform user distribution. Moreover, we also show that the transmission rate from each helper to its attached users is at most a constant factor away from the corresponding lower bound. To our knowledge, this is the first work in hierarchical networks to eliminate the additive gap of the second layer. Finally, simulation results demonstrate the superiority of the proposed caching scheme compared with the state of the art.

Semantic communication has drawn substantial attention as a promising paradigm to achieve effective and intelligent communications. However, efficient image semantic communication encounters challenges with a lower testing compression ratio (CR) and signal-to-noise ratio (SNR) compared to the training phase. To tackle this issue, we propose an innovative semantic knowledge base (SKB)-enabled generative semantic communication system for image classification task and image generation task. Specifically, a lightweight SKB, comprising class-level information, is exploited to guide the semantic communication process, which enables us to transmit only the relevant indices. This approach promotes the completion of the image classification task at the transmitter and significantly reduces the transmission load. Meanwhile, the class-level knowledge in the SKB facilitates the image generation task by allowing controllable generation, making it possible to generate class-consistent images in resource-constrained and low SNR scenarios. Furthermore, an adaptive CR and mode selection mechanism is designed to automatically adjust the CR and task mode, which allows the proposed system accommodate various CR and SNR conditions. Evaluation results indicate that the proposed method outperforms the benchmarks and achieves superior performance with minimal CR and SNR.

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we first introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on Earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user could access one or more satellites at the same time. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic wrap-around fashion. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections given the satellite constellation, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to select user groups and construct request patterns, such that the connections between users and satellites involved could be decoupled. Moreover, the gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results show the superior performance of the proposed scheme and converse bound.

This paper proposes a replacement algorithm for file caching in mobile edge computing (MEC) networks. While there are numerous schemes for file replacement, it remains a challenge to achieve good, robust, and predictable performance simultaneously. To address this challenge, we introduce a general scheme called Incremental Least-Recently-Used (iLRU), which builds on the classic Least-Recently-Used (LRU) algorithm. iLRU initially caches only a “portion” of the file upon the first request and incrementally caches more when there are more requests for the file. In this regard, the request frequency can be inferred from the cached size without incurring additional overhead, where a larger cached size represents a higher request frequency. We derive the theoretical hit ratio of iLRU based on the Time-to-Live (TTL) analysis. With the Time-to-Live (TTL) analysis, we can theoretically derive the hit ratio and properties of iLRU and notably show that iLRU allocates more cache space to popular files, resulting in a higher hit ratio than LRU. Simulation results demonstrate the superior performance of iLRU and validate the accuracy of the theoretical hit ratio. Furthermore, we conduct simulations over various real-world traces to show that iLRU outperforms existing schemes across various real-world traces, defenestrating the robustness of iLRU.

Entanglement distribution via probabilistic entanglement swapping across a quantum repeater chain connecting two quantum nodes is a challenging problem. The difficulty lies in the exponential number of possible swapping structures within the repeater chain, necessitating efficient search algorithms, especially as the chain length increases. In this paper, we first explore the algorithmic design to facilitate the search for the optimal swapping structure along a repeater chain, aiming to maximize the entanglement distribution rate. Second, we examine the computational complexities of various algorithms and find that prior approaches exhibit excessively high complexities. Thus, we propose an efficient dynamic programming-based algorithm, FastHED, that leverages heuristics to expedite the search for the optimal swapping structure. Our theoretical analysis reveals that the upper bound of the proposed algorithm’s computational complexity is$O(n(\log n)^{3})$(more precisely,$O(n(\log n)^{2} \log \log n)$when$n\le 2^{29}$), a significant improvement over the existing algorithm with a complexity of$O(n^{2} \log n)$, where n denotes the repeater chain’s length. Additionally, we design a best-first framework to evaluate the performance of different algorithms. Numerical results show that our algorithm achieves a higher average entanglement distribution rate than existing algorithms.

Network performance monitoring and troubleshooting are crucial yet challenging tasks in datacenter management. Despite the numerous solutions that have been proposed in recent years, their efforts are often hindered by high costs and unreliable failure localization, making it difficult to deploy them in real-world environments. In this paper, we presentLMon, a highly reliable and efficient system for monitoring and troubleshooting in datacenter networks. LMon utilizes the characteristic of ECMP hashing linearity to control probe packet routing, enabling the monitoring of targeted paths without any modification of underlying protocols and devices. Additionally, LMon leverages a lightweight probing technique to reduce monitoring overhead, as well as integrates the improved LASSO regression and hypothesis testing for higher accuracy and faster processing in link failure localization. We evaluate the performance of LMon in our testing environment. Compared to the monitoring system Pingmesh, LMon generates only one-third probes while maintaining 99% accuracy and 1% false negatives.

We study the stochastic combinatorial semi-bandit problem with unrestricted feedback delays under merit-based fairness constraints. This is motivated by applications such as crowdsourcing, and online advertising, where immediate feedback is not immediately available and fairness among different choices (or arms) is crucial. We consider two types of unrestricted feedback delays: reward-independent delays where the feedback delays are independent of the rewards, and reward-dependent delays where the feedback delays are correlated with the rewards. Furthermore, we introduce merit-based fairness constraints to ensure a fair selection of the arms. We define the reward regret and the fairness regret and present new bandit algorithms to select arms under unrestricted feedback delays based on their merits. We prove that our algorithms all achieve sublinear expected reward regret and expected fairness regret, with a dependence on the quantiles of the delay distribution. We also conduct extensive experiments using synthetic and real-world data and show that our algorithms can fairly select arms with different feedback delays.

Semantic communication has emerged as a promising communication paradigm and there have been extensive research focusing on its applications in the increasingly prevalent multi-user scenarios. However, the knowledge discrepancy among multiple users may lead to considerable disparities in their performance. To address this challenge, this paper proposes a novel multi-pair cooperative semantic knowledge base (SKB) update policy. Specifically, for each pair endowed with SKB-enabled semantic communication, its well-understood knowledge in the local SKB is selected out and uploaded to the server to establish a global SKB, via a score-based knowledge selection scheme. The knowledge selection scheme achieves a balance between the uplink transmission overhead and the completeness of the global SKB. Then, with the assistance of the global SKB, each pair’s local SKB is refined and their performance is improved. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains with minimal transmission overhead, especially for the initially poor-performing pairs.

Coded caching was introduced in partially cooperative D2D networks where some selfish users keep silent during delivery process. In existing works, unselfish users were randomly selected as delivery proxies for selfish users, resulting in asymmetric utilization of unselfish users. We observe that averaging the transmission load uniformly over unselfish users may achieve better performance. With this motivation, we propose scheme A, which symmetrically employs all unselfish users in delivery, and its transmission rate outperforms the schemes in existing works. Moreover, existing schemes applied the same symmetric cache placement and file splitting strategy as in the fully cooperative D2D networks, which ignored the asymmetry brought by the silent selfish users and thus incurred extra transmission. Consequently, we propose scheme B with an asymmetric file division strategy where the subfiles are exclusively designated to be sent by unselfish users, thus eliminating the proxy transmissions. To evaluate the performance of proposed schemes, a new converse bound is derived by the index coding approach. The joint performance of schemes A and B in certain regime is shown to be exact-optimal under uncoded placement when$S=1$, where$S$represents the number of selfish users. Similar to existing works, schemes A and B require an assumption that$S\leq t-1$, where$t$represents the caching redundancy. When$S\geq t$, we further propose scheme$C$employing uncoded placement, which outperforms the existing MDS-code based scheme in certain regime due to reduction on coding overhead. Numerical simulations are conducted to verify the superior performance of the proposed schemes.

Coded caching is a novel technique to reduce network traffic by exploiting multicast opportunities over users. Practical networks may have two layers of caches, where the central server is first connected to an internal node called a “mirror” (e.g., a base station or wifi router) and this internal node links to end users through a broadcast channel. Previous work [1] shows that there exists a tension between rates in these two layers, and the optimal tradeoff is obtained in a simplified model with one mirror and two users. In this paper, we further consider a more general setting with one mirror and multiple users. The performance limits of coded caching on two-layer networks are obtained under uncoded placement. For converse bounds, it is shown that a linear combination of the worst-case rates in each layer is not less than a threshold. An optimal achievable scheme is designed accordingly to match the novel converse bounds.

Mobile edge computing (MEC) and digital twin (DT) technologies have been recognized as key enabling factors for the next generation of industrial Internet of Things (IoT) applications. In existing works, DT-assisted edge network resource optimization solutions mostly focus on short-term performance optimization, and long-term resource optimization has not been well studied. Thus, this paper introduces a digital twin-assisted heterogeneous edge network (DTHEN), aiming to minimize long-term energy consumption by jointly optimizing transmit power and computing resource. To solve the stochastic optimization problem, we propose a long-term queue-aware energy minimization (LQEM) scheme for joint communication and computing resource management. The proposed scheme uses Lyapunov optimization to transform the original problem with long-term time constraints into a deterministic upper bound problem for each time slot, decouples it into three independent sub-problems, and solves each sub-problem separately. We then theoretically prove the asymptotic optimality of the LQEM scheme and the tradeoff between system energy consumption and task queue backlog. Finally, experimental results verify the performance analysis of the LQEM scheme, demonstrating its superiority over several benchmark schemes, and reveal the impact of various parameters on the system.

Coded caching is an effective method to reduce the traffic load on network bottleneck. While the heterogeneities on the number of requests and cache sizes in coded caching have been studied independently, their joint impact is still unclear. This paper investigates coded caching in scenarios with heterogeneous number of requests and cache sizes. We propose two achievable schemes. The first scheme, based on file grouping and multi-round decentralized coded caching, is demonstrated to be order optimal under the worst setting, i.e., when user with the i-th smallest cache has the i-th largest number of requests. Moreover, we obtain an important insight that the lower bound of the achievable rate is predominantly influenced by users with high$\frac {X_{i}}{M_{i}}$ratios, where$X_{i}$and$M_{i}$represent the number of requests and cache size of user i, respectively. Since the achievable rate of our first scheme is difficult to analyze in the general setting, we further propose the second scheme to derive a tractable upper bound. Based on the insight, the second scheme rearranges the users according to their$\frac {X_{i}}{M_{i}}$ratios and employs a threshold to divide them into the head users who may have a large impact on the lower bound, and the tail users who may have a small impact on the lower bound. The server transmits the demands of the head users directly while applying the first scheme in groups among the tail users. Under the general setting, the gap between the rate of our second scheme and the lower bound is proved to be within a logarithmic factor. Simulations are conducted to verify the superior performance of our proposed schemes.

Network performance monitoring and troubleshooting is a crucial but challenging task in datacenter management. Despite the numerous solutions that have been proposed in recent years, their efforts are often hindered by high costs and unreliable fault localization, making it difficult to deploy them in real-world environments. In this paper, we present LMon, a highly reliable and efficient system for monitoring and troubleshooting in datacenter networks. LMon utilizes the characteristic of ECMP hashing linearity to control the packet routing without any modification of the underlying protocols. Additionally, LMon leverages a lightweight probing technique to reduce monitoring overhead. Furthermore, the system integrates improved LASSO regression and statistical hypothesis testing for higher accuracy and faster processing in link failure localization. The effectiveness of LMon is demonstrated through its implementation and evaluation in ns-3 simulation. The results validate the reliability and efficiency of the system, making it a promising option for ensuring long-term network maintenance in datacenters.

Coded caching is a promising technique for reducing traffic load in wireless networks. In this paper, coded caching is studied for heterogeneous lossy channels, where each user independently suffers packet loss with a distinct and fixed probability. In the original MAN transmission [1], each packet is destined for a specific combinational subset of users. When a packet is received by users beyond the destined subset, it is undecodable and will be dropped. We term these packets as the Overheard Packets (OPs). Interestingly, we find that the OPs can be exploited as side information to increase the multicast gain of the retransmitted packets. With this motivation, we propose a reliable coded caching scheme to reduce the retransmission rate over the shared link. This method enables the users to recover their own lost packets (could be different) from one encoded retransmitted packet. Aided by the OPs, the multicast gain of retransmissions can be increased up to K in certain cases, where K represents the number of users. In other cases, retransmission achieves at least the same multicast gain as the original transmission. Two well-known techniques for reliable transmission are employed in coded caching and serve as baseline schemes. Through both simulations and theoretical analysis we show that the proposed scheme significantly outperforms the baseline schemes in terms of the retransmission rate.

360-degree videos have gained considerable popularity by offering immersive experiences to viewers. However, they consume significantly high bandwidth and demand for specialized caching schemes at the edge network. Previous caching schemes are limited as they either rely on complete historical data or neglect the viewer-varying characteristics in 360-degree videos. In this paper, we present an online caching scheme for 360-degree videos that leverages feedback from sequentially arriving viewers at the network edge. Our scheme consists of two components: an online tile popularity prediction component that accurately predicts the popularity of the tiles with the PopPred algorithm, and an online tile-bitrate caching optimization component that optimizes caching decisions to enhance viewers’ quality of experience (QoE) with the CacheOpt algorithm. We prove that both algorithms have sublinear regret, i.e., $O(\sqrt K ),$ where K is the number of viewers. We also conduct comprehensive experiments using real-world data to show that our caching scheme achieves better performance with lower regrets, higher QoE, and higher hit ratios compared with existing algorithms.

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on the earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user can access one satellite. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic shift pattern. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to construct request patterns such that the connections between users and satellites involved could be decoupled. The gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results for the performance of our scheme are also demonstrated.

Coded caching technique is an effective method to reduce peak traffic. We consider coded caching in combination networks in which a server with a library of N files connects to users through h relay nodes and each user with cache connects to different sets of r relay nodes. Previous works are always assumed that the users’ cache capacity is consistent, but this is quite different from the actual application scenarios. The cache capacity of users connected to the network may vary widely. In this paper, we investigate combination networks with heterogeneous caches. For this setting, we propose a decentralized coded caching scheme for any combination network and derive the upper bound of transmission rate. Then, we derive the lower bound for any transmission scheme. We also analyze the gap between upper and lower bounds under the case that the user cache sizes follow Zipf distributions. For Zipf distributions with α not less than 1, we can obtain a constant gap, while when α is less then 1, the gap is bounded by 28 log N if $r \geq \frac{h}{{\log N}}$.

Coded caching is an effective way to reduce the network load by exploiting multicast opportunities between distinct users. In [1], a hierarchical network with two layers of caches is investigated. It is shown that the achievable rate of each layer is bounded from the corresponding converse bound within a constant multiplicative and additive gap, and can be achieved simultaneously. In this regard (with the additive gap), there is no tension between the rates of the two layers [1]. This paper takes a further investigation on this topic and shows that there is tension using a toy model. With the toy model, we derive new lower bounds and propose novel achievable schemes, which are shown to be optimal in an average sense. The involved techniques in both the lower bounds and achievable schemes could be of interest for future studies on coded caching in hierarchical networks.

Coded caching has been studied extensively and extended to many scenarios because it can exploit multicast opportunities to reduce the downlink traffic on the network. The basic model of coded caching is that a server with$N$files of the same size, is connected to several users with cache size$M$through a shared link. Prior work [1] has derived the optimal rate-memory tradeoff of this system with the constraint of uncoded prefetching. However, when heterogeneity of file sizes is taken into account, there remains open questions. In this paper, we consider two users and$N$files with distinct file sizes. When the cache size is large or small compared to the overall file size, i.e., 0 ≤$M$≤ ½ N F1or$M$≥ H(W) - ½ N F1where$F$1denotes the minimum file size and H (W) represents the overall information entropy of all files in the database, we show that our proposed scheme is optimal under all possible schemes. When the cache size is mediate, i.e., ½ N F1$M$1we show that our proposed scheme is optimal under the constraint of uncoded prefetching. To obtain these results, we introduce new techniques to extend the novel cut-set bound in [2], and propose a new achievable scheme based on both files and caches sizes.

Coded caching is an effective method to reduce the traffic load on the network bottleneck link by exploiting the joint optimization of caching and transmission. In most of previous works, one user may request only one file. However, users could have distinct number of requests in practice. The number of files requested by the users may also follow different distributions. In this paper, we investigate coded caching with distinct number of user requests. We propose a decentralized coded caching transmission scheme for user requests whose number may follow an arbitrary distribution and analyze the upper bound of the transmission rate. We also derive the corresponding lower bound for any achievable scheme. We show that the gap between these two rates is within a constant factor of 12. And this result holds for any distribution on the number of user requests. For the proposed transmission scheme and a given distribution, we show that when the number of users exceeds a threshold, the transmission rate will be bounded without regard to the number of users, due to the benefit of multicast opportunities brought by coded caching. Simulation results demonstrate the superior performance of the proposed scheme.

Coded caching is a caching technique proposed recently, which can greatly reduce backhaul traffic by exploiting the multicast transmissions over different users, even when they request distinct files. The transmission gain of coded caching depends on the distribution of users' cache size and also the distribution of file popularity. While the impacts of user caches or file popularity has been investigated independently, their joint impact on coded caching is unknown and challenging to be analyzed. In this paper, we propose a scheme considering the cache sizes distribution and file popularity distribution simultaneously. The average transmission rate and the lower bound are both analyzed. The gap between the two rates is at most an O(log K) factor, where K is the number of users. Simulation results show superior performance of the proposed scheme.

Coded caching can exploit multicast opportunities even when multiple users request different pieces of content, and thus can significantly reduce the backhaul requirement to serve high-volume content. A common assumption in existing studies of coded caching is that all files are with the same size, which however may not be true in reality. Our previous work [1] first studied this problem, and proposed a non-trivial lower bound, as well as a new achievable scheme that uses a caching probability increasing proportionally with the file size. However, the gap [1] of the achievable rate and the lower bound still differs by a factor of Θ(log K), where K is the number of users in the system. In this paper, under a mild assumption that the total size of all files is larger than eight times the size of one individual cache, we will close this gap and reduce it to a constant by proposing a novel new lower bound and another new achievable scheme. Our lower bound is derived by considering a new cut-set bound, where files of a type1will be requested more often if the number of such files is smaller. Our achievable scheme uses a caching probability that decreases with the number of files with a same type. The improvements on both the lower bound and the achievable rate make their gap constant.

Due to the random and dynamic energy-harvesting process, it is challenging to conduct optimal rate control in Energy-Harvesting Communication Systems (EHCSs). Existing works mainly focus on two cases: (1) the traffic load is infinite (as long as there is energy, there is data to transmit), in which the objective is to optimize the rate control policy subject to the dynamic energy arrivals, thus maximizing the average system throughput; and (2) the traffic load is finite, in which the objective is to optimize the rate control policy, thus minimizing the time by which all packets are delivered. In this work, we focus on the optimal rate control of EHCSs from another important and practical perspective, where the data and energy arrivals are both random. Given any deadline of T , our goal is to maximize the total throughput in [0, T ]. Specifically, two scenarios are considered: (1) energy is ready before the transmission; and (2) energy arrives randomly during the transmission. In both scenarios, we assume that the data arrive randomly during the transmission. For the first scenario, we develop a novel Stepwise Searching Algorithm (SSA) based on the cumulative curve methodology, which is shown to achieve the optimal solution and the complexity grows only linearly with the problem size. In addition, the SSA can provide a simple and appealing graphical visualization of approximating the optimal solution. For the second scenario, we provide a simplified case study that can be solved by the SSA with low computation overhead and demonstrate the difficulties in solving the general setting, which initiates a first step toward the full understanding of the scenario when energy arrives randomly during the transmission.

Erasure codes provide higher reliability than replication for a same level of redundancy to store data in distributed storage systems, yet with more bandwidth overhead. Recently, regenerating codes are introduced, which significantly reduce the repair bandwidth by analyzing the fundamental tradeoff between storage capacity and repair bandwidth via the information flow graph. In reality, distributed storage systems with hierarchical structures are more common in data centers where data are organized in racks, and the cross-rack communication is more costly than the in-rack communication. Hence, in this paper, we introduce a class of codes to repair a failed node by downloading data from nodes in the same rack only, which are termed as multi-rack regenerating codes (MRC). Different with existing works, the cross-rack repair bandwidth under our codes can be reduced to zero. Meanwhile, we obtain the optimal tradeoff between storage and bandwidth of MRC, and present an explicit construction of MRC with the common product-matrix framework.

Network function virtualization (NFV) can significantly reduce the operation cost and speed up the deployment for network services to markets. Under NFV, a network service is composed by a chain of ordered virtual functions, or we call a "network function chain." A fundamental question is when given a number of network function chains, on which servers should we place these functions and how should we form a chain on these functions? This is challenging due to the intricate dependency relationship of functions and the intrinsic complex nature of the optimization. In this paper, we formulate the function placement and chaining problem as an integer optimization, where each variable is an indicator whether one service chain can be deployed on a configuration (or a possible function placement of a service chain). While this problem is generally NP-hard, our contribution is to show that it can be mapped to an exponential number of min-cost flow problems. Instead of solving all the min-cost problems, one can select a small number of mapped min-cost problems, which are likely to have a low cost. To achieve this, we relax the integer problem into a fractional linear problem, and theoretically prove that the fractional solutions possess some desirable properties, i.e., the number and the utilization of selected configurations can be upper and lower bounded, respectively. Based on such properties, we determine some "good" configurations selected from the fractional solution and determine the mapped min-cost flow problem, and this helps us to develop efficient algorithms for network function placement and chaining. Via extensive simulations, we show that our algorithms significantly outperform state-of-art algorithms and achieve near-optimal performance.

Distributed storage systems provide reliability by distributing data over multiple storage nodes. Once a node fails, a new node is introduced to the system to maintain the availability of the stored data. The new node downloads information from other surviving nodes called helper nodes to recover the lost data in the failed node. The number of helper nodes is called repair degree. Compared to traditional approaches, e.g., replication and erasure codes, the regenerating codes proposed recently can significantly reduce the repair bandwidth in homogeneous distributed storage systems. Most existing works focus on uniform settings (e.g., in terms of repair degree and repair bandwidth). However, due to network structures or connectivity limitations, for each failed node, the number of required helper nodes may be different for distinct failed nodes. Furthermore, considering the limits of network traffic of bandwidth, the amount of information allowed to be downloaded from each helper node could also vary. Thus we are motivated to investigate heterogeneous distributed storage systems where the repair degree and the amount of information downloaded from each helper node can be different. In order to obtain the minimal bandwidth to recover a failed node, we construct an information flow graph for such heterogeneous systems. By analyzing the cut-set bound of the information flow graph, the optimal tradeoff between storage capacity and repair bandwidth is derived. We then propose asymmetric regenerating codes that can achieve the curve of the optimal tradeoff. A linear construction of asymmetric regenerating codes is presented. Compared with previous regenerating codes, asymmetric regenerating codes are shown to have a lower repair bandwidth under a certain constraint condition, whose reduction can be up to 36.2%.

Caching plays an important role in reducing the backbone traffic when serving high-volume multimedia content. Recently, a new class of coded caching schemes have received significant interest, because they can exploit coded multi-cast opportunities to further reduce backbone traffic. Without considering file popularity, prior works have characterized the fundamental performance limits of coded caching through a deterministic worst-case analysis. However, when heterogeneous file popularity is considered, there remain open questions regarding the fundamental limits of coded caching performance. In this paper, for an arbitrary popularity distribution, we first derive a new information-theoretic lower bound on the expected transmission rate of any coded caching schemes. We then show that a simple coded-caching scheme attains an expected transmission rate that is at most a constant factor away from the lower bound. Unlike other existing studies, the constant factor that we derived is independent of the popularity distribution.

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

Coded caching has demonstrated the superiority in mitigating traffic pressure through jointly considering content delivery and storage schemes. However, existing works mainly focus on the situation where users have uniform demands with multiple layer of caches. In this paper, we propose a multilevel hierarchical coded caching scheme when users have nonuniform demands in a multi-hop content delivery network scenario. Specifically, to maintain the symmetry constraint of coded caching we utilize K-means to separate the file set with arbitrary distribution of popularity into several file subsets. We also formulate the Jensen's inequality and derive the upper bound of the transmission rate in each layer. To evaluate the system efficiency, we leverage the open-source Netflix dataset as our file set and conduct an experiment on a content delivery network with two layers of caches. Experimental results demonstrates that our multilevel hierarchical coded caching scheme performs much better than the baseline LFU caching scheme.

Recently, performance analysis for large-scale content-centric mobile ad hoc networks (MANETs) has received intense attention. In content-centric MANETs, content delivery consists of two operations, i.e., content placement and content retrieval, which may involve different network costs. However, the existing performance studies in content-centric MANETs mainly focus on content retrieval, and hence may not reflect the impact of content placement. In this paper, we investigate the asymptotic throughput and delay performance by considering the two operations of possibly different network costs. In particular, we introduce a general weighted sum delay cost of content placement and content retrieval as the delay performance metric. We consider an arbitrary content popularity distribution and study two mobility models in different time scales, i.e., fast and slow mobility. For each mobility model, we characterize the impacts of the network parameters on the network performance. By optimizing the content placement and retrieval for contents of different popularities, we design a general near-optimal scheme, the parameters of which reflect the delay weights of the two phases. We show that the network performance improves as the number of cached replicas increases until the number reaches a threshold. Finally, we show that our results are general and can incorporate some existing results as special cases.

Since it was found that real mobility processes exhibit significant degree of correlation (correlated mobility) and nodes are often heterogeneously distributed in clustered networks (cluster scalability), there has been a great interest in studying their impact on network performance, such as throughput and delay. However, limited works have been done to investigate their impact jointly, which may due to the challenges in capturing both features under a unified network model. In this paper, we focus on their impact on asymptotic connectivity and propose correlated mobile k-hop clustered network model. Two connectivity metrics are considered. One is network connectivity with probability (w.p.). The other is connectivity almost surely (a.s.), which requires a stronger condition than connectivity with probability. With mobility correlation and cluster scalability vary, we show that there are three distinct states for network connectivity, i.e., cluster-sparse, cluster-dense state, and cluster-inferior dense state, respectively. We first prove the exact value of the critical transmission range for each state, respectively, and then further generalize the three states into a unified one, which we call it cluster mixed state. The critical transmission range for connectivity almost surely is √2 times the range for connectivity with probability. Our main contribution lies in how to group correlated nodes into independent ones in various settings, and reveals the interrelated relationship between correlated mobility and cluster scalability through state transitions.

To accommodate exponentially increasing traffic demands, operators are seeking to offload cellular traffic to small base stations (BSs) in heterogeneous cellular networks (HCNs), which is promising in alleviating traffic congestion. In HCNs, operators are eager to balance the traffic globally, where users may be pushed to less preferred small BSs, resulting in possible conflict with user local preference. Thus, it is a big challenge to achieve dynamic load balancing for operators and provide participation incentive for users simultaneously. Due to the dynamics of network state and user traffic demand, we are inspired to utilize Lyapunov optimization to develop a congestion-aware cellular offloading scheme. Specifically, an operator profit maximization problem involving network selection and rate control is formulated. To achieve long-term network stability, we propose a congestion-aware network selection algorithm, obtaining the BS alternative set that maintains traffic congestion constraint. By exploring the heterogeneity of user quality sensitivity, we devise the optimal quality-price contract, which maximizes operator profit. With effective pricing and resource allocation, users are motivated to make proper association strategy chosen from the BS alternative set. Simulation results demonstrate the effectiveness of our scheme in improving operator profit. User incentive and network stability are also validated.

Social characteristics have been observed to significantly affect the communications in wireless social networks, especially that within a group following the multicast manner. To model the multicast group in wireless social networks, we should incorporate two important social characteristics, i.e., social relationship and group size. In most existing works, the modeling of social relationship only considers geographic factor. However, such models fail to well characterize wireless social networks, since unlike that in traditional wireless networks, geographic distance is no longer the major factor that affects people's communications and some non-geographic factors, such as user popularity, become more and more important. Moreover, group size is always assumed to be known a priori in previous works, which cannot fully meet the realistic condition. Therefore, in this paper, we model the multicast group from a combination of geographic and non-geographic (GN) perspectives. Specifically, we propose the GN Model to characterize social relationship and the independently-selected model to characterize group size. In addition to the geographic distance considered in the modeling of social relationship, we also introduce user popularity which reflects the influence of each user on others. Then, we assume that the source transmits the data packet to all his friends following the multicast manner. Based on the proposed model, we calculate transmission distance and network traffic load, and then discuss how they are influenced by both geographic and GN factors. Moreover, our proposed models are verified through experimental measurements based on real datasets.

In this paper, we investigate the impact of information-theoretic secrecy constraint on the capacity and delay of mobile ad hoc networks (MANETs) with mobile legitimate nodes and static eavesdroppers whose location and channel state information (CSI) are both unknown. We assume n legitimate nodes move according to the fast i.i.d. mobility pattern and each desires to communicate with one randomly selected destination node. There are also nνstatic eavesdroppers located uniformly in the network and we assume the number of eavesdroppers is much larger than that of legitimate nodes, i.e., ν > 1. We propose a novel simple secure communication model, i.e., the secure protocol model, and prove its equivalence to the widely accepted secure physical model under a few technical assumptions. Based on the proposed model, a framework of analyzing the secrecy capacity and delay in MANETs is established. Given a delay constraint D, we find that the optimal secrecy throughput capacity is ~Θ(W((D/n))(2/3)), where W is the data rate of each link. We observe that: 1) the capacity-delay tradeoff is independent of the number of eavesdroppers, which indicates that adding more eavesdroppers will not degenerate the performance of the legitimate network as long as ν > 1; 2) the capacity-delay tradeoff of our paper outperforms the previous result Θ((1/nψe)) in , where ψe=nν-1=ω(1) is the density of the eavesdroppers. Throughout this paper, for functions f(n) and g(n), we denote f(n)=o(g(n)) if limn→∞(f(n)/g(n))=0; f(n)=ω(g(n)) if g(n)=o(f(n)); f(n)=O(g(n)) if there is a positive constant c such that f(n) ≤ cg(n) for sufficiently large n; f(n)=Ω(g(n)) if g(n)=O(f(n)); f(n)=Θ(g(n)) if both f(n)=O(g(n)) and f(n)=Ω(g(n)) hold. Besides, the order notation ~Θ omits the polylogarithmic factors for better readability.

This paper investigates the multicast capacity of static wireless social networks. We adopt the two-layer network model, which includes the social layer and the networking layer. In the social layer, the social group size of each source node is modeled as power-law distribution. Moreover, the rank-based model is utilized to describe the relation between source and destinations in the networking layer. Based on the two-layer network model, the probability density function (PDF) of the destination positions is analyzed and verified by numerical simulation, which is different from the traditional ad hoc networks. According to the PDF, the bound of the network capacity is derived, and we propose a Euclidean minimum-spanning-tree-based transmission scheme, which is proved to achieve the order of capacity bound for most cases. Finally, the capacity of social networks is compared to the traditional multicast ad hoc networks, which indicates that the capacity scaling performs better in social networks than traditional ones. To our best knowledge, this is the first work of analyzing the impact on the capacity of social relation and group size in multicast ad hoc networks for the rank-based model.

Delayed traffic offloading is a promising paradigm to alleviate the cellular network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to users’ mobility. How to exploit user delay tolerance to improve the profit of operator as well as mobile users becomes a big challenge. In this paper, we model this delayed offloading process as a monopoly market based on contract theory, where operator acts as the monopolist setting up the optimal contract by statistical information on user satisfaction. We propose an incentive mechanism to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses a proper quality–price contract item according to its type. More specifically, we investigate this delayed offloading scheme under strongly incomplete information scenario, where user type is private information. We derive an optimal contract, which maximizes operator’s profit for both the continuous-user-type model and the discrete-user-type model. Numerical results validate the effectiveness of our incentive mechanism for delayed traffic offloading in cellular networks.

In this paper, we derive the critical transmission range, i.e., the smallest transmission distance of nodes such that wireless network can be connected, in large-scale clustered wireless networks. Contrary to most previous literature on independent and homogeneous mobility of nodes, we consider general settings with inhomogeneous node distribution and correlated mobility. In particular, we consider three network states based on the degree of correlation among nodes, i.e., cluster-sparse state (strong correlations), cluster-dense state (weak correlations), and cluster-transitional state (medium correlations). Under each state, we focus on the following problems: 1) how to place cluster-head nodes to minimize the critical transmission range and 2) what is the corresponding minimum critical transmission range. We derive the optimal distribution of cluster-head nodes that minimizes the critical transmission range, and show that the inhomogeneous distribution of mobile nodes leads to a smaller critical transmission range.

This paper studies the impacts of social relationships and inhomogeneous node distribution on wireless network performance. Motivated by the social characteristic that makes nodes more likely to communicate with nearby nodes, we present a model that captures the small-world property, power-law distribution, and multi-clustering topology of wireless networks. We compute the average traffic distance to derive the optimal per-node capacity, which is then analyzed from the social perspective. Based on the communication pattern, we introduce the quasi-strong ties and quasi-weak ties in order to discover how system parameters control the amount of network flows with different strengths of social relationships. Moreover, we propose an indicator of information propagation speed (IIPS), and show that there is a tradeoff between the IIPS and the per-node capacity of overall nodes.

Traffic offloading over Delay Tolerant Networks (DTNs) is a promising paradigm to alleviate the network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to user's mobility. How to exploit delay tolerance to improve the profit of the operator as well as mobile users becomes a big challenge. In this paper, we investigate the problem of the interrelation of delay and user QoS. Inspired by contract theory, we model the delayed offloading process as a monopoly market where the operator makes pricing by considering statistical information about user satisfaction. In addition, we propose an incentive framework to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses an appropriate quality-price contract item according to its type. Moreover, we derive an optimal contract which is feasible and maximizes the operator's profit as well. Numerical results validate the effectiveness of our incentive framework for traffic offloading over DTNs.

Coded caching can exploit new multicast opportunities even when multiple users request different pieces of content, and thus can significantly reduce the backhaul requirement for serving high-volume content. However, existing studies of coded caching have been limited to the scenarios where all files of interest are of a common size. This work studies the performance limits of coded caching when the file sizes are different. We derive a new lower bound and an achievable upper bound for the worst-case transmission rate under coded caching, and show that these two bounds differ by at most a Θ(log K) factor, where K is the number of users in the system. There are two key novelties in our analysis. First, our lower bound is derived by considering a new cut-set bound where larger files are requested more times. The analysis of this new cut-set bound requires careful concatenation of several entropy inequalities. Compared to a lower bound using standard cut-set arguments, our lower bound is improved by a Θ(log K) factor. Second, our achievable scheme uses a caching probability that increases proportionally with the file size. Compared to schemes that use a common caching probability, the achievable rate of our scheme is reduced by a Θ K/logk2factor.

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(nβ/3log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.

With the advent of smart portable devices and location-based applications, user's mobility pattern is found to be highly dependent on varying locations. In this paper, we analyze asymptotic throughput-delay performance of mobile ad hoc networks (MANETs) under a location popularity based scenario, where users are more likely to visit popular locations. This work provides a complementary perspective compared with previous studies on fundamental scaling laws for MANETs, mostly assuming that nodes move uniformly in the network. Specifically, we consider a cell-partitioned network model with cells of known popularity, which follows a Zipf's law distribution with popularity exponent α. We first conduct the analysis under traditional store-carry-forward paradigm, and find that location heterogeneity affects the network performance negatively, which is due to the waste of potential transmission opportunities in popular cells. Motivated by this observation, we further propose a novel store-carry-accelerate-forward paradigm to enhance the network communication, exploiting these potential transmissions. Theoretical results demonstrate that our proposed scheme outperforms all delay-capacity results obtained in conventional scheme for any α. In particular, when α = 1, it can achieve a constant capacity with an average delay of Θ(√n) (except for a polylogarithmic factor), while the delay is Θ(n) in conventional scheme. And by letting α = 0, our results can cover Neely's scaling laws. Moreover, we show that the delay-capacity tradeoff ratio satisfies ≥Θ(√n), revealing that exploiting location popularity can effectively improve the performance in MANETs.

In this paper,1we investigate the throughput and delay in heterogeneous cognitive radio networks (HCRN), where the data source and the destination (S-D) is heterogeneously distributed following a rank based model and secondary users (SUs) provide relay service for primary users (PUs). We consider two scenarios: 1) PUs and SUs are both static; and 2) PUs are static and SUs are mobile. For scenario 1, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with the S-D heterogeneity exponent α, which depicts the variation of different heterogeneous extents of S-D distribution. In addition, the delay of both primary and secondary networks are shown to be altering with α. Further, we reveal that the number of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput. For scenario 2, we utilize a modified uniform mobility (MUM) model to depict the motion of SUs and mainly focus on the analysis of throughput and delay for primary networks. It shows that the primary network throughput is also free of the heterogeneous extent of S-D distribution, while the delay changes with α. Due to the mobility of SUs, a better delay-throughput tradeoff of primary networks is achieved compared with that in scenario 1.

This paper investigates random walk mobility models with location heterogeneity, where different locations may have different neighboring regions. We consider$n$locations in a one-dimension network and investigate two cases, i.e.,full-range locationswhere nodes situated have the capability to shuffle throughout the network andlong-range locationswhere nodes are allowed to move to positions nearby within a certain range. In the former situation, with the exact expressions derived, we find location heterogeneity has a critical impact on the first hitting time of random walk, varying from$\Theta (n)$to$\Theta \left(n^3\right)$according to different extent of heterogeneity. The result covers, as two special cases, both the classic independent and identically distributed (i.i.d) mobility and traditional random walk when varying the number of full-range locations. In the latter one, our asymptotic results on both the first crossing time and cover time suggest that they are inversely proportional to the range of neighboring region$r$($\propto r^{-2}$and$\propto r^{-1}$, respectively). Furthermore, with multiple concurrent random walks introduced, the first hitting time can be drastically decreased and the effect is strengthened if combined with location heterogeneity. In addition, our investigation into the stationary distribution of nodes indicates that the uniformity no longer holds due to different transition probabilities, as a result of location heterogeneity. We also conduct extensive simulation results to verify our observations and enhance the understanding on the impact of network parameters. Based on the insights obtained, we move forward to investigate the impact of location heterogeneity in two-dimension networks.

In the asymptotic analysis of large scale mobile networks, most previous works assume that nodes move in all the cells identically. We put forward this line of research by considering location popularity, which is verified by recent experimental studies. Nodes tend to visit some popular locations and go to other locations less frequently. We first analyze its multicast capacity and delay under the traditional 2-hop store-carry and forward paradigm. With different location popularity distributions, network capacity and delay will vary according to the distribution exponent. As the popularity becomes more diverse, less concurrent transmissions are tolerated in the network, which brings down the performance. Observing that transmission opportunity is not fully utilized in popular cells for 2-hop paradigm, we put forward a 3-hop scheme, which is called store-carry-accelerate-forward scheme. In this 3-hop scheme, each packet is first sent to an initial relay, who carries the packets into the popular cells and then broadcasts to multiple nodes to accelerate the delivery. By so doing, we showed that the 3-hop scheme outperforms the 2-hop scheme for all popularity distributions. Furthermore, we study the delay-capacity tradeoffs for multicast under both schemes and the buffer needed for stability requirement. Our results reveals the joint impact of multicast and location heterogeneity on the design of transmission schemes, and may shed new insights for future studies.

In this paper, we focus on the capacity and delay tradeoff for multicast traffic pattern in cognitive radio mobile ad hoc networks (MANETs). In our system model, the primary network consisting of n primary nodes overlaps with the secondary network consisting of m secondary nodes in a unit square. Assume that all nodes move according to an independent and identically distributed mobility model, and each primary node serves as a source that multicasts its packets to kp primary destination nodes, whereas each secondary source node multicasts its packets to ks secondary destination nodes. Under the cell partitioned network model, we study the capacity and delay for the primary networks under two communication schemes, i.e., noncooperative scheme and cooperative scheme. The communication pattern considered for the secondary network is cooperative scheme. Given that m = nβ(β > 1), we show that per-node capacities O(1/kp) and O(1/ks) are achievable for the primary network and the secondary network, with average delays Θ(n log kp) and Θ(m log ks), respectively. Moreover, to reduce the average delay in the secondary network, we employ a redundancy scheme and prove that a per-node capacity O(1/ks√m log ks) is achievable with average delay Θ(√m log ks). We find that the fundamental delay-capacity tradeoff in the secondary network is delay/capacity ≥ O(mkslog ks) under both cooperative and redundancy schemes.

Cognitive radio (CR) technique is considered an effective mechanism to relieve the spectrum scarcity issue, where the secondary users (SUs) can utilize the idle spectrum of the primary users (PUs). How the performance of the wireless network will be influenced by the introduction of CR technique has been attracting much attention in past years. While many efforts have been made to study the cognitive radio network, where the data source and the destination (S-D) is homogeneously distributed, the research on cognitive radio networks (CRN) with heterogeneous S-D distribution is still very limited. In this paper, we investigate the throughput and delay scaling law in the heterogeneous cognitive radio network (HCRN), where the S-D pair follows a rank based model and SUs provide relay service for PUs in reciprocating the utilization of PUs' idle spectrum. By applying a cellular TDMA scheduling scheme, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with respect to the S-D heterogeneity exponent denoted by α. In addition, the delay scaling are derived for both primary and secondary networks and shown to be altering in accordance with α. Further, we reveal that the density of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput.

Increasingly, more spectrum bands are utilized for unlicensed use in wireless cognitive networks. It is important to study how information-theoretic secrecy capacity is affected in large-scale cognitive networks. We consider two scenarios: (1) non-colluding case, where eavesdroppers decode messages individually. In this case, we propose a new secure protocol model to analyze the transmission opportunities of secondary nodes. We show that the secrecy capacity of the primary network is not affected, while the secondary network can achieve the same performance as a standalone network in the order sense. Since our analysis is general as we only make a few relaxed assumptions on both networks, the conclusions hold when both networks are classic static networks, networks with i.i.d mobility, multicast networks etc. (2) colluding case where eavesdroppers can collude to decode a message. In that case, we show that the lower bound of per-node secrecy capacity of the primary network is Ω(1/√n φe-2/α-1(n)) when the eavesdropper density is φe(n)=Ω(log2n). Interestingly the existence of secondary nodes increases the secrecy capacity of the primary network.

We propose the correlated mobile k-hop clustered networks model to implement correlated node movements and scalable clusters. We divide network states into three categories, i.e., cluster-sparse state, cluster-dense state and cluster-inferior dense state, and achieve the critical transmission range for the last two states. Furthermore, we find that correlated mobility and cluster scalability are closely related with each other and the impact of these two properties on connectivity is mainly through influencing network state transition.

In this paper, we study the throughput and delay in wireless cognitive social networks. Specifically, we consider a common scenario for cognitive radio networks (CRNs) where the primary and secondary networks operate at the same time and space and share the spectrum. On this basis, we integrate a social relationship into the CRN where each source node selects its destination upon a rank-based model, which captures the social characteristic well. By applying a cellular time-division multiple-access scheduling scheme, we first characterize the distinct traffic pattern caused by the social relationships between nodes. Then, we derive the achievable throughput and delay for both primary and secondary networks under the new network setting. In addition, we also study the cognitive social networks with infrastructure where I = o1(n) base stations are regularly deployed within the primary network. Given a probabilistic routing strategy, throughput of the proposed network is recalculated. Particularly, due to the social relationships between nodes, we reveal that a larger I is required if we expect a significant capacity gain within the primary network compared with previous works.

In this paper, we give a global perspective of multicast capacity and delay analysis in Mobile Ad Hoc Networks (MANETs). Specifically, we consider four node mobility models: (1) two-dimensional i.i.d. mobility, (2) two-dimensional hybrid random walk, (3) one-dimensional i.i.d. mobility, and (4) one-dimensional hybrid random walk. Two mobility time-scales are investigated in this paper: (i) fast mobility where node mobility is at the same time-scale as data transmissions and (ii) slow mobility where node mobility is assumed to occur at a much slower time-scale than data transmissions. Given a delay constraint$D$, we first characterize the optimal multicast capacity for each of the eight types of mobility models, and then we develop a scheme that can achieve a capacity-delay tradeoff close to the upper bound up to a logarithmic factor. In addition, we also study heterogeneous networks with infrastructure support.

Since wireless channel is vulnerable to eavesdroppers, the secrecy during message delivery is a major concern in many applications such as commercial, governmental, and military networks. This paper investigates information-theoretic secrecy in large-scale networks and studies how capacity is affected by the secrecy constraint where the locations and channel state information (CSI) of eavesdroppers are both unknown. We consider two scenarios: 1) noncolluding case where eavesdroppers can only decode messages individually; and 2) colluding case where eavesdroppers can collude to decode a message. For the noncolluding case, we show that the network secrecy capacity is not affected in order-sense by the presence of eavesdroppers. For the colluding case, the per-node secrecy capacity of Θ([1/(√n)]) can be achieved when the eavesdropper density ψe(n) is O(n-β), for any constant β > 0 and decreases monotonously as the density of eavesdroppers increases. The upper bounds on network secrecy capacity are derived for both cases and shown to be achievable by our scheme when ψe(n)=O(n-β) or ψe(n)=Ω(log[(α-2)/(α)]n), where α is the path-loss gain. We show that there is a clear tradeoff between the security constraints and the achievable capacity. Furthermore, we also investigate the impact of secrecy constraint on the capacity of dense network, the impact of active attacks and other traffic patterns, as well as mobility models in the context.

This paper characterizes an evolving network with growing number of nodes, which are associated with each other through social relations but employ transmission via wireless communications. The network exhibits social relations between nodes by attaching a new arriving node to a sample node already existing as well as some nodes the sample directly links to. We study capacity in this evolving network in terms of both geographic distribution of nodes and traffic patterns. Our results show that in heterogeneous geographic distribution where nodes having strong social relations tend to locate more closer, the corresponding capacity is significantly impacted by both social relations and network evolution. In particular, with appropriate control on the initial number of nodes as well as links between them, it is even possible to achieve almost constant per-node capacity except for a poly-logarithmic factor. For homogeneous geographic topology where nodes' positions exhibit uniform distribution, social relations among nodes is useless for improving capacity, which is mainly affected by network evolution and therefore degrades sharply with time. To our best knowledge, this is the first work studying capacity from the perspective of social evolving network.

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks, which can explore the general scaling laws of throughput capacity but cannot predict the exact achievable throughput. In this paper, we investigate the non-asymptotic capacity of multihop wireless networks for multicast applications wherein for each source node, k nodes are randomly selected as receivers. Since multicast routing has a dynamic nature, it is challenging for the exact performance analysis. To tackle the problem, we propose an explicit analytical model which describes multicast transmissions, considers networks of arbitrary size, takes data burst into account, and also covers the notion of time scales for transient analysis. By developing a practical multicast scheme, stochastic network calculus is employed for the exact analysis. With the analytical model, we derive lower and upper bounds on multicast capacity, which are non-asymptotic functions of the above variables, and also recover the scaling laws from an asymptotic point of view. Simulations further verify the accuracy of the analytical bounds.

This paper investigates random walk mobility models with location heterogeneity, where different locations may have different neighboring regions.With the assumption of totally n locations, we consider two cases, i.e., full-range locations where nodes situated have the capability to shuffle throughout the network and long-range locations where nodes are allowed for moving to positions nearby within a certain range. In the former situation, with the exact expressions derived, we find location heterogeneity has a critical impact on the first hitting time of random walk, varying from Θ(n) to Θ(n3) according to different extent of heterogeneity. The result covers, as two special cases, both the classic independent and identically distributed (i.i.d) mobility and traditional random walk as we vary the number of full-range locations. In the latter one, our asymptotic results on both the first crossing time and cover time suggest that they are inversely proportional to the range of neighboring region r (∝ r−2and ∝ r−1, respectively). Furthermore, extensive simulation is conducted to verify our observations and enhance the understanding on the effect of network parameters.

In this paper, we consider a general scenario where tphe wireless network is modeled as a rectangle with side lengths √n and n1/2−β, where 0 ≤ β ≤ 1/2 is a variance independent of n. n source-destination pairs are randomly located in the network, with their communication subject to the short-distance SNR, the long-distance SNR and the path loss. Based on these conditions, we identify the scaling laws of capacity for the network. The upper bound of the capacity is derived for the network, with the adoption of Multiple Input Multiple Output (MIMO) technology. Furthermore, we propose three different schemes, i.e., multihop, MIMO and hybrid schemes to achieve the upper bound. The capacity performance exhibits distinctive intriguing results as the side length of the network varies. Moreover, our results capture the impact of network shape on capacity and can unify the previous capacity results obtained in square networks.

Since wireless channel is vulnerable to eavesdroppers, the secrecy during message delivery is a major concern in many applications such as commercial, governmental and military networks. This paper investigates information-theoretic secrecy in large-scale networks and studies how capacity is affected by the secrecy constraint where the locations and channel state information (CSI) of eavesdroppers are both unknown. We consider two scenarios: 1) non-colluding case where eavesdroppers can only decode messages individually; and 2) colluding case where eavesdroppers can collude to decode a message. For the non-colluding case, we show that the network secrecy capacity is not affected in order-sense by the presence of eavesdroppers. For the colluding case, the per-node secrecy capacity of Θ(1/√n) can be achieved when the eavesdropper density ψe(n) is O(n-β), for any constant β >; 0 and decreases monotonously as the density of eavesdroppers increases. The upper bounds on network secrecy capacity are derived for both cases and shown to be achievable by our scheme when ψe(n) = O(n-β) or ψe(n) = Ω(log α-2/α n), where α is the path loss gain. We show that there is a clear tradeoff between the security constraints and the achievable capacity.

In this paper, we define multicast for an ad hoc network through nodes' mobility as MotionCast and study the delay and capacity tradeoffs for it. Assuming nodes move according to an independently and identically distributed (i.i.d.) pattern and each desires to send packets to$k$distinctive destinations, we compare the delay and capacity in two transmission protocols: one uses 2-hop relay algorithm without redundancy; the other adopts the scheme of redundant packets transmissions to improve delay while at the expense of the capacity. In addition, we obtain the maximum capacity and the minimum delay under certain constraints. We find that the per-node delay and capacity for the 2-hop algorithm without redundancy are$\Theta(1/k)$and$\Theta(n\log k)$, respectively; for the 2-hop algorithm with redundancy, they are$\Omega(1/k\sqrt{n\log k})$and$\Theta(\sqrt{n\log k})$, respectively. The capacity of the 2-hop relay algorithm without redundancy is better than the multicast capacity of static networks developed by Li [IEEE/ACM Trans. Netw., vol. 17, no. 3, pp. 950–961, Jun. 2009] as long as$k$is strictly less than$n$in an order sense, while when$k=\Theta(n)$, mobility does not increase capacity anymore. The ratio between delay and capacity satisfies delay/rate$~~\geq O(nk\log k)$for these two protocols, which are both smaller than that of directly extending the fundamental tradeoff for unicast established by Neely and Modiano [IEEE Trans. Inf. Theory, vol. 51, no. 6, pp. 1917–1937, Jun. 2005] to multicast, i.e., delay/rate$~~\geq O(n k^2)$. More importantly, we have proved that the fundamental delay–capacity tradeoff ratio for multicast is delay/rate$~~\geq O(n\log k)$, which would guide us to design better routing schemes for multicast.