Research Interest (In alphabetical order)
Algorithms for Complex Networks
Communication Networks
Complex Networks
Design and Deployment of Citizens Broadband Radio Service (CBRS) Networks
Network Science
Next-generation Wireless Network Design
Softwarized 5G Networks
Wireless Mesh Networks
Ongoing Research Directions
- Design and deployment of next-generation wireless networks on CBRS spectrum
- Incentive mechanism design for CBRS networks
Research Outcomes (Summary of each research work is provided below)
On temporal and spatial behaviors of CBRS SAS (For details, please refer to J12 in Publications)
The recently established Citizens Broadband Radio Service (CBRS) has attracted attention because it accommodates incumbents, auctioned license users, and unlicensed users. Incumbents retain unconstrained transmission rights, but when inactive, the spectrum is shared between the Primary Access License (PAL) users and the General Authorized Access (GAA) users. A cloud-based centralized administrator controls the spectrum sharing, Spectrum Access System (SAS), using an environmental sensing capability network to establish transmission rights for PAL and GAA users without hindering the incumbents. This work reported findings from GAA CBRS Devices' (CBSDs') deployments in California's San Diego County, which has a large incumbent presence and characterizes temporal and spatial behaviors of CBRS SAS based on measured temporal data. We proposed a one-step time-homogeneous Markov chain-based model that effectively captures the hidden state machine of the SAS-CBRS authentication steps from the measured logs. We then estimated the stationary distributions of existing process states and expected values of hitting and return times for CBRS spectrum availability. Further, by studying the CBRS spectrum availability at various locations in San Diego County, we found evidence of undisclosed obfuscation in reporting the spectrum availability by the SAS administrator. Finally, two novel optimal spectrum obfuscation strategies were proposed to maximize the entropy of the Markov Chain's hidden states and provide more significant obfuscation than the undisclosed obfuscation strategy of the SAS.
On identifying optimal shortcut edge locations in a linear small-world wireless sensor network (For details, please refer to C17 in Publications)
Wireless sensor networks (WSNs) consist of power-constrained sensor nodes deployed for various data collection operations in real-world settings. The WSN nodes collect real-time data and send it to a data processing unit (DPU) for in-depth analysis of the collected data with multihop relaying. For sparsely deployed WSNs, data collected from nodes many hops away from the DPU suffer significant delays, thus making such deployments unsuitable for delay-critical applications. However, adding shortcut edges (SEs) directly to the DPU can alleviate the end-to-end transmission delay by incorporating small-world properties. In this work, we analytically found the optimal locations of a few SEs on a finite-sized linear WSN. Moreover, we also characterized the associated tradeoff in transmit power for using these SEs in the existing linear WSN.
On reliability of CBRS communications near U.S. Navy installations in San Diego (For details, please refer to C16 in Publications)
This work reported findings from two real-world unlicensed General Authorized Access (GAA) device installations in the recently released incumbent-dominated Citizens Broadband Radio Service (CBRS) shared spectrum in San Diego County, a region with a strong naval incumbent presence. We quantified the impact of the incumbents on GAA users by developing a one-step time-homogeneous Markov chain-based model to track the state of the systems, estimating the transition probabilities from measured data, and measuring the stationary distribution and expected values of hitting time and return time. Our measured datasets-based analyses showed that the transmission rights of the deployed CBRS Devices (CBSDs) remained inactive for more than half the experiment duration. Also, the return time to get back the transmission rights is more than 13.5 hours for both locations. We also noted that the cloud-based centralized Spectrum Access System (SAS) administrator of the CBRS shared spectrum often obfuscates the available spectrum information and revokes CBRS communication rights without prior information from the auctioned Primary Access License (PAL) users and unlicensed GAA users to protect the incumbents' location details and their movements. As a result, the communication reliability of the non-incumbents (i.e., PAL and GAA) was affected by the current policy frameworks concerning only aggregate interference mitigation using the environmental sensing capability networks to protect the incumbents.
Resilient and latency-aware orchestration of network slices using multi-connectivity in MEC-enabled 5G networks (For details, please refer to J11 in Publications)
Network slicing and multi-access edge computing (MEC) are new paradigms that play key roles in 5G and beyond networks. In particular, network slicing allows network operators (NOs) to divide available resources into multiple logical network slices (NSs) to provide dedicated virtual networks tailored to specific service/business requirements. MEC enables NOs to provide diverse ultra-low latency services to support the needs of different industry verticals by moving computing facilities to the network edge. An NS can be constructed/deployed by instantiating a set of virtual network functions (VNFs) on top of MEC cloud servers for provisioning diverse latency-sensitive/time-critical communication services (e.g., autonomous driving and augmented reality) on demand at a lesser cost and time. However, VNFs, MEC cloud servers, and communication links are subject to failures due to software bugs, misconfiguration, overloading, hardware faults, cyber-attacks, power outages, and natural/artificial disasters. Failure of a critical network component disrupts services abruptly and leads to users’ dissatisfaction, which may result in revenue loss for the NOs. In this work, we presented a novel approach based on multi-connectivity in 5G networks to tackle this problem. Our proposed approach is resilient against i) failure of VNFs, ii) failure of local servers within MEC, iii) failure of communication links, and iv) failure of an entire MEC cloud facility at the regional level. To this end, we formulated the problem as a binary integer programming (BIP) model to deploy NSs optimally with the minimum cost and proved it was NP-hard. Since the exact optimal solution for the NP-hard problem cannot be efficiently computed in polynomial time, we proposed an efficient genetic algorithm-based heuristic to obtain a near-optimal solution in polynomial time. Extensive simulations showed that our proposed approach reduces resource wastage and improves throughput while providing high resiliency against failures.
Reliable placement of service function chains and virtual monitoring functions with minimal cost in softwarized 5G networks (For details, please refer to J10 in Publications)
Network functions virtualization (NFV) allows the softwarization of network functions and enables the running of network functions as virtual network function (VNF) instances on top of the cloud infrastructure. In softwarized 5G networks, communication services can be realized through service function chains (SFCs) in which multiple VNFs are connected in sequential order as per the requirements, which offer flexibility, agility, and dynamic management and orchestration of networks. However, softwarization and cloudification of networks using NFV introduce new challenges in terms of reliability and availability due to software bugs, hardware failures, malfunction of VNFs, and service degradation. In this work, we first explored latency-aware and reliable SFC placement to meet users' requirements and enhance the reliability of SFCs from VNF failures. Then, we focused on the reliable placement of virtual monitoring functions at proximal locations of VNFs to identify and mitigate service degradation and security-related issues in the network. To this end, we formulated the problems as integer linear programming (ILP) problems to minimize the total deployment cost and showed that they are NP-hard. To overcome the high computational complexity of the ILP problems, we proposed novel heuristic algorithms based on complex network theory to provide near-optimal solutions in polynomial time for large input instances. By extensive simulations, we showed that our proposed algorithms provide a near-optimal solution (optimality gap is 5%) in a real-world network topology.
Latency-aware and survivable mapping of VNFs in 5G network edge cloud (For details, please refer to C15 in Publications)
Network functions virtualization (NFV) and multi-access edge computing (MEC) play crucial roles in 5G networks for dynamically provisioning diverse communication services with heterogeneous service requirements. In particular, while NFV improves flexibility and scalability by softwarizing physical network functions as virtual network functions (VNFs), MEC enables the provision of delay-sensitive/time-critical services by moving computing facilities to the network edge. However, these new paradigms introduce latency, availability, and resource allocation challenges. In this work, we first explored MEC cloud facility location selection and then latency-aware placement of VNFs in different selected locations of NFV-enabled MEC cloud facilities to meet the ultra-low latency requirements of different applications (e.g., tactile Internet, virtual reality, and mission-critical applications). Furthermore, we also aimed to guarantee the survivability of VNFs and an edge server against failures in resource-limited MEC cloud facilities due to software bugs, configuration faults, etc. To this end, we formulated the problem of latency-aware and survivable mapping of VNFs in different MEC cloud facilities as an integer linear programming (ILP) to minimize the overall service provisioning cost and showed that the problem is NP-hard. Owing to the high computational complexity of solving the ILP, we proposed a simulated annealing-based heuristic algorithm to obtain a near-optimal solution in polynomial time. With extensive simulations, we showed the effectiveness of our proposed solution in a real-world network topology, which performs close to the optimal solution.
On achieving capacity-enhanced small-world networks (For details, please refer to J9 in Publications)
Networks, whether communication or transportation, often suffer from significant degradation in average network flow capacity (ANFC) due to one or more bottleneck nodes. A few long-ranged links connecting distant nodes in a regular network enhance ANFC and incorporate small-world characteristics. However, the existing deterministic long-ranged link addition strategies based on minimum average path length, maximum betweenness centrality, or maximum closeness centrality cannot guarantee significant improvement in ANFC. In this work, we proposed an exhaustive search-based long-ranged link (LL) addition algorithm, maximum flow capacity (MaxCap), which deterministically maximizes ANFC based on the maximum flow (of information or objects) among node-pairs in the context of weighted undirected networks. Furthermore, based on the observations from MaxCap, we proposed a new LL addition heuristic, average network flow capacity enhancement using small-world characteristics (ACES), which significantly enhances ANFC and the LL length-type product and improves traffic load distribution in a weighted undirected network. We validated the performance of our LL addition method through exhaustive simulations on various arbitrary networks and real-world road transportation networks. ACES can be applied to many real-world applications in communication, transportation, and tactical networks where ANFC is a critical parameter.
An efficient scheme for constructing small-world machine-to-machine networks (For details, please refer to C14 in Publications)
Real-world Machine-to-Machine (M2M) networks comprise power-constrained sensor nodes to collect data of interest and send it to data aggregators for further processing using multi-hop relaying, which increases end-to-end transmission delay. However, a strict deadline for the delay must be met in many real-world application scenarios, such as low-latency communication in a 5G network. In this work, we proposed Constrained Link Addition Using high Sensor nodes (CLAUSe) to efficiently construct a few long-ranged links among the chosen high sensor nodes (sensor nodes with at least two interfaces) to reduce end-to-end hop distance in a sparse random M2M network setting, thereby, incorporating the small-world characteristics.
An efficient malware detection technique using complex network-based approach (For details, please refer to C13 in Publications)
System security is becoming an indispensable part of our daily life due to the rapid proliferation of unknown malware attacks. Recent malware was found to have a very complicated structure that is hard to detect by traditional malware detection techniques such as antivirus, intrusion detection systems, and network scanners. In this work, we proposed a complex network-based malware detection technique, Malware Detection using Complex Network (MDCN), that considers the Application Program Interface Call Transition Matrix (API-CTM) to generate complex network topology and then extracts various feature sets by analyzing different metrics of the complex network to distinguish malware and benign applications. The generated feature set is then sent to several machine learning classifiers, which include naive Bayes, support vector machine, random forest, and multi-layer perceptron, to comparatively analyze the performance of the MDCN-based technique. The analysis revealed that MDCN shows higher accuracy, with lower false-positive cases, when the multi-layer perceptron-based classifier is used to detect malware. MDCN technique can efficiently be deployed in the design of an integrated enterprise network security system.
Corporate linkages and financial performance: A complex network analysis of Indian firms (For details, please refer to J8 in Publications)
Corporate governance has a significant impact not only on the behavior and performance of a corporation but also on the functioning of other corporations, entrepreneurialism in the economy, and the working of capital markets. One way of looking at the effects of corporate governance on the company's performance is to study the contributions of the corporate board of directors. In this work, we explored the network of the director boards for the top Indian corporations to better understand the effects of the interlocking of management boards when financial performance is considered. To study the corporate networks, we collected and analyzed relevant datasets of the top 150 Indian companies based on the maximum revenue generation. We found that the debt-to-equity ratio, on average, tends to decrease as the measure of the degree centrality of certain corporations increases. Further, we studied a modularity maximization-based community structure of the network of the director boards to better comprehend the interconnections of the Indian corporates. Moreover, the underlying relationship between modularity and corporate performance is also analyzed, and it can be observed that most of the top Indian corporations with low net current asset value per share belong to larger communities.
Social network aware dynamic edge server placement for next-generation cellular networks (For details, please refer to C12 in Publications)
A social network group in a 4th Generation Long-Term Evolution (4G LTE) network consists of multiple users who share information in the social network group, and the information is sent to other users through the unicast transmission. As a result, a large amount of operational bandwidth is consumed due to the redundant data transmissions. Thus, the operational latency of the network increases. We aim to reduce bandwidth consumption and minimize operational latency by optimizing message transmission over a 4G LTE network with edge computing. Our experiments found that placing edge servers at all the edge locations reduces the transmission delay by approximately 92.64%, along with a 51.38% improvement in the network throughput. However, installing edge servers at all the edge locations to reduce the transmission of redundant information is not a feasible and cost-effective solution. In this work, we proposed a novel edge server placement strategy, Social network Aware Dynamic Edge Server placement (SADES), which uses the information from the overlay social network groups to efficiently identify a few influential base stations to place the edge servers in an existing 4G LTE network. We observe that when a single edge server is placed in an influential base station with SADES, the transmission delay is reduced by approximately 68.02%, along with a 42.45% improvement in the network throughput as compared to an existing 4G LTE network infrastructure with no edge servers. SADES can be utilized to design efficient, ultra-reliable, and low latency communication-enabled~5th Generation (5G) networks.
MACA-I: A malware detection technique using memory management API call mining (For details, please refer to C11 in Publications)
Present-day malicious software, often encountered in an Internet-enabled electronic system, is extremely difficult to detect by the existing malware detection solutions. In this work, we introduced a novel malware detection strategy, Memory management with API CAll mIning (MACA-I), that efficiently detects malware based on dynamic analysis. MACA-I analyzes API calls responsible for accessing the system memory and generates required features by observing the transitions of memory management APIs and the allocated memory block space during the runtime. We found that MACA-I is approximately 95.45% accurate while detecting malware programs based on system memory-related runtime behavior.
On the evolution of finite-sized complex networks with constrained link addition (For details, please refer to C10 in Publications)
Scale-free characteristics, where the degree distribution of a network follows the power-law distribution, are observed in most of the existing real-world complex networks. Barabási and Albert first studied the evolution of random complex networks and observed that complex networks with node growth via preferential attachment can evolve to be scale-free. However, some complex networks, such as neural networks inside the human brain, employees of an organization, and networks of closed social groups, can be considered finite-sized complex networks that are relatively static concerning the number of nodes where only the number of edges grow with time. This work studied the gradual evolution of such finite-sized complex networks. It can be observed from our study that a finite-sized complex network, with average path optimal edge growth, evolves as the following: a regular network → a small-world network → a scale-free network → a scale-free network with the truncated degree distribution → a fully connected network with unconstrained link addition. Therefore, it can be concluded that in finite-sized complex networks, edge growth can result in transitional scale-free networks.
ACTM: API call transition matrix-based malware detection method (For details, please refer to C9 in Publications)
Traditional malware detection techniques, such as signature-based detection and traditional antivirus software, are not beneficial for detecting many recent malware threats. In this work, we proposed a novel malware detection technique, API call transition matrix-based malware detection (ACTM), that efficiently detects malware based on their runtime behavior. We found that the ACTM technique performs better and detects malware with approximately 95.23% accuracy. ACTM can find applications in designing real-time malware detection when an enterprise network security system is concerned.
Deterministic evolution through indexed leaf node based attachment in complex networks (For details, please refer to C8 in Publications)
Complex networks are abstract graphs where the nodes are real-world entities and their relationships can be imitated as links. The relationships among various real-world entities are neither entirely random nor fully regular; thus, the structures of evolving complex networks are non-trivial. To study the characteristics of such evolving complex networks, efficient models that emulate real-world networks are needed. This work proposed a novel network model, deterministic evolution through leaf node attachment (DELNA), that can efficiently emulate many real-world networks, such as technological networks, social networks, and other artificial networks. We also compare our DELNA-based network model with a few existing network evolution models, namely the Barabási-Ravasz-Vicsek deterministic network model and the Barabási-Albert network evolution model. DELNA can find applications in the Internet of Things and satellite communications, where network resilience is a very crucial parameter.
GFT centrality: A new node importance measure for complex networks (For details, please refer to J7 in Publications)
Identifying central nodes is crucial to designing efficient communication networks or recognizing key individuals of a social network. In this work, we introduced Graph Fourier Transform Centrality (GFT-C), a metric that incorporates local and global characteristics of a node, to quantify the importance of a node in a complex network. GFT-C of a reference node in a network is estimated from the GFT coefficients derived from the importance signal of the reference node. Our study reveals the superiority of GFT-C over traditional centralities such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and Google PageRank centrality, in the context of various arbitrary and real-world networks with different degree–degree correlations.
Link influence entropy (For details, please refer to J6 in Publications)
In this work, we proposed a new metric, Link Influence Entropy (LInE), which describes the importance of each node based on the influence of each link present in a network. The influence of a link can neither be effectively estimated using betweenness centrality nor degree-based probability measures. The proposed LInE metric, which effectively estimates the influence of a link in the network and incorporates this influence to identify nodal characteristics, performs better than degree-based entropy. We found that LInE can differentiate various network types, which degree-based or betweenness centrality-based node influence metrics cannot. Our findings show that spatial wireless networks and regular grid networks have the lowest and highest LInE values. Finally, performance analysis of LInE was carried out on a real-world network and on a wireless mesh network testbed to study the influence of our metric and the influence stability of nodes in dynamic networks.
A multi-backup path protection scheme for survivability in elastic optical networks (For details, please refer to J5 in Publications)
Two important challenges in designing a survivable optical network are minimizing backup spectrum allocation and ensuring spectrum assignment constraints. Allocating backup spectrum is one important approach for survivable optical network design. Connection requests that are rejected due to the unavailability of a single backup path can be survived using multiple backup routes. Multiple backup routes increase connection acceptance rate and improve backup resource sharing. In this work, we presented a strategy for survivability that optimizes primary and backup spectrum allocations and multiple backup route assignments for surviving a connection request. In our strategy, named Backup Spectrum Reservation with MultiPath Protection (BSR-MPP), multiple backup routes are searched over advanced reserved backup resources when an optical connection is concerned. Simulation results show that confinement of backup resources results in higher resource sharing and assignment of multiple backup lightpaths. It can also be observed that BSR-MPP has lower Bandwidth Blocking Probability and higher spectrum efficiency as compared to conventional Shared Path Protection (SPP) and MultiPath Protection (MPP) strategies.
On spectral analysis of node centralities (For details, please refer to C7 in Publications)
In this work, we studied spectral properties of node centralities, such as degree, closeness, and betweenness centralities, using the graph Fourier transform. We considered node centralities as signals on various networks, namely, regular, random, small-world, and scale-free networks. The spectral analysis helped us to easily understand centrality patterns over various networks. We observe spectral patterns for different network models and subsequently classify networks from the spectra of node centralities.
Analytical identification of anchor nodes in a small-world network (For details, please refer to J4 in Publications)
Adding new links in a communication network or forming new social ties in a social network is an important way to improve the performance of the respective networks. In this work, we consider the optimal link addition for a string network to minimize the average path length for the network. In prior work, the optimal addition of links to a string network has been found to lead to a network where all the added links are incident to a single anchor node. Furthermore, the position of the anchor node is fixed at either approximately 0.2 or 0.8 of the total number of nodes in the string network. In this letter, we provide an analytical justification for this observation and, in the process, identify the fixed fractional positions of the anchor nodes. We also discuss the significance of the anchor nodes for a string network, which is an important network model for several real-world communication and social networks.
Graph Fourier transform based on directed Laplacian (For details, please refer to C6 in Publications)
In this work, we redefine the graph Fourier transform (GFT) under the DSP G framework. We consider the Jordan eigenvectors of the directed Laplacian matrix as graph harmonics and the corresponding eigenvalues as the graph frequencies. For this purpose, we propose a shift operator based on the directed Laplacian of a graph. Based on our shift operator, we then defined the total variation of graph signals used for frequency ordering. We achieve natural frequency ordering and interpretation via the proposed definition of GFT. Moreover, we show that our proposed shift operator makes linear shift-invariant (LSI) filters under DSP G to become polynomials in the directed Laplacian.
Conflict graph based community detection (For details, please refer to C5 in Publications)
Community is a network’s subgraph where vertices share similar properties and reflect interesting characteristics for understanding complex networks more closely. Therefore, community structure analysis is important in understanding and exploring complex networks and helps describe relationships among network nodes. However, efficiently finding communities in a complex network remains an open problem. Since there exist numerous ways of defining a community, existing strategies have adopted different parameters to reflect the varied behavior of a community structure and try to give a coarser or finer community distribution. In this work, we proposed Conflict graph Transform based Community Detection (CTCD) strategy to improve the quality of community distributions. CTCD focuses on the impact of the degree of influence to detect more favorable community partitions. A well-known measure, Surprise, is used to evaluate and compare the quality of the community distributions obtained using CTCD. Finally, CTCD was applied to real-world networks to study the performance and usefulness of our strategy. Using CTCD, we obtained better community distributions with higher Surprise values in real-world networks. We observe that 1-hop and 2-hop influences improve the Surprise value in higher and lower average clustering coefficient networks. Moreover, CTCD can efficiently extract the hierarchical nature of communities within networks.
Influence of greedy reasoning on network evolution (For details, please refer to C4 in Publications)
Power-law degree distribution is observed in many real-world complex networks; thus, they are called scale-free networks. Barabási et al. first observed these scale-free characteristics in natural and human-made networks and stated that network growth and preferential attachment of new network nodes are the two key ingredients for transforming a real-world network into a scale-free network. In this work, we observed that adding links while greedily optimizing an appropriate objective is another reason for transforming a regular network into a scale-free one. Moreover, we observed that pure random addition could not realize scale-free networks.
An efficient heuristics to realize near-optimal small-world networks (For details, please refer to C3 in Publications)
Small-world characteristic brings down the average path length of a network by adding a few long links among network node pairs. In a real-world deployment scenario, probabilistic long-link addition cannot guarantee the optimal value of average path length for a network with a limited number of long links. In this work, we proposed a generalized heuristic, Sequential Deterministic Long-link Addition (SDLA) algorithm to incorporate small-world properties for moderate-sized string topology networks. Our proposed algorithm has O(k × N) time complexity compared to O(N^2(k+2) × log N) for optimal and O(k × N^4 × log N) for near-optimal long-link addition strategies for k long links when a string topology network of size N is concerned. Our studies show that the SDLA algorithm negligibly deviates in various network properties (e.g., average path length, average clustering coefficient, and graph centralities) from the optimal and near-optimal solutions.
Communication overhead of an OpenFlow wireless mesh network (For details, please refer to C2 in Publications)
OpenFlow is a new paradigm for running experimental protocols on production networks. It is becoming a standard reference for the implementation of software-defined networking. OpenFlow can be deployed on a wireless mesh network, a multi-hop relaying wireless network, for efficient network traffic management. Furthermore, various challenges, such as load-balancing and control traffic management of wireless mesh networks, can be effectively handled with the OpenFlow central controller. In this work, we develop an OpenFlow wireless mesh network testbed to investigate various performance metrics (e.g., throughput, latency, CPU usage, and control traffic) for full mesh and partial mesh network topologies. Furthermore, we measure empirical controller capacity, a new metric we defined to evaluate the OpenFlow wireless mesh network controller performance. We also address various implementation challenges of real-world OpenFlow wireless mesh networks.
The reason behind the scale-fee world (For details, please refer to J3 in Publications)
Most real-world networks existing in nature or observed in the world of technology follow the power-law degree distribution. Thus, they are called scale-free networks. Barabási first observed that the scale-free network is formed by preferential attachment of new nodes in the existing network. Therefore, a new node is more likely to connect with a node with a higher neighbor degree in the network. In this work, we found that greedy decision-making is one of the key characteristics for transforming a regular network into a scale-free one. Greedy decision-making results in long-ranged link affinity, a phenomenon responsible for hub-node creation in the network. Moreover, we show that pure random addition of new links in a regular network does not result in a scale-free network.
Delay optimized small-world networks (For details, please refer to J2 in Publications)
Many regular networks suffer significant delays due to large end-to-end hop distances among source and destination nodes. However, the presence of a few long-ranged links transforms a regular network into a small-world network and, thus, optimizes network delay by minimizing end-to-end hop distance. In this work, we study various deterministic long-ranged link addition strategies (e.g., based on average path length, average edge length, betweenness centrality, closeness centrality, and closeness centrality disparity) to incorporate small-world characteristics as well as to optimize the average network delay of the network. Moreover, we analyze time complexity to assess the efficiency of each strategy in detail. We observe, in an N node network, that deterministic long-ranged link addition by closeness centrality disparity (CCD) strategy is only O(N^2 × log N) time complex compared to other optimal long-ranged link addition strategies, which take O(N^4 × log N) time to achieve similar performance in the context of average network delay.
Load-aware routing for non-persistent small-world wireless mesh networks (For details, please refer to C1 in Publications)
Wireless mesh network is a distributed multi-hop relaying network. A large-scale wireless mesh network typically has a high value of network average path length, which results in reduced throughput and increased delay in the network. Average path length can be reduced in the network by implementing a few long links among the network node pairs, thus introducing the small-world characteristics in the wireless mesh networks. However, the conventional routing algorithms are not optimized for small-world wireless mesh networks. In this work, we propose a Load-aware Non-Persistent small-world long-link Routing (LNPR) algorithm for small-world wireless mesh networks to achieve lower average transmission path length for data transfer sessions among a set of source-node and destination-node pairs in the network. LNPR uses a load balancing strategy to better distribute the network traffic among the normal and non-persistent long links in the small-world wireless mesh networks to efficiently use long links, which are precious data transmission paths. LNPR provides 58% to 95% improvement in call blocking probability and 23% to 70% in maximum load reduction with increments ranging from 0.7% to 9% increase in average transmission path length. Small-world wireless mesh networks find numerous applications in rural and community networks for cost-effective communication.
Capacity calculation and sub-optimal power allocation scheme for OFDM-based systems (For details, please refer to J1 in Publications)
For emerging cellular wireless systems, mitigating inter-cell interference is the key to achieving a high capacity and good user experience. This work was devoted to the performance analysis of interference mitigation techniques for the downlink channel in an orthogonal frequency division multiple access (OFDMA) network, focusing on the Long Term Evolution-Advanced (LTE-A) standard. We derive a general closed-form equation of the system capacity, considering multiple cells, and investigate a coordination technique for interference mitigation.
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