End-to-End Latency Prediction for General-Topology Cut-Through Switching Networks

Low latency networking is gaining attention to support futuristic network applications like the Tactile Internet with stringent end-to-end latency requirements. In realizing the vision, cut-through (CT) switching is believed to be a promising solution to significantly reduce the latency of today's store-and-forward switching, by splitting a packet into smaller chunks called flits and forwarding them concurrently through input and output ports of a switch. Nevertheless, the end-to-end latency performance of CT switching has not been well studied in heterogeneous networks, which hinders its adoption to general-topology networks with heterogeneous links. To fill the gap, this paper proposes an end-to-end latency prediction model in a heterogeneous CT switching network, where the major challenge comes from the fact that a packet's end-to-end latency relies on how and when its flits are forwarded at each switch while each flit is forwarded individually. As a result, traditional packet-based queueing models are not instantly applicable, and thus we construct a method to estimate per-hop queueing delay via M/G/c queueing approximation, based on which we predict end-to-end latency of a packet. Our extensive simulation results show that the proposed model achieves 3.98-6.05% 90th-percentile error in end-to-end latency prediction

Efficient Identification and Utilization of Spectrum Opportunities in Cognitive Radio Networks.

There has been an exponential increase in spectrum demands due to new emerging wireless services and applications, making it harder to find unallocated spectrum bands for future usage. This potential resource scarcity is rooted at inefficient utilization of spectrum under static spectrum allocation. Therefore, a new concept of dynamic spectrum access (DSA) has been proposed to opportunistically utilize the legacy spectrum bands by cognitive radio (CR) users. Cognitive radio is a key technology for alleviating this inefficient spectrum utilization, since it can help discover spectrum opportunities (or whitespaces) in which legacy spectrum users do not temporarily use their assigned spectrum bands. In a DSA network, it is crucial to efficiently identify and utilize the whitespaces. We address this issue by considering spectrum sensing and resource allocation. Spectrum sensing is to discover spectrum opportunities and to protect the legacy users (or incumbents) against harmful interference from the CR users. In particular, sensing is an interaction between PHY and MAC layers where in the PHY-layer signal detection is performed, and in the MAC-layer spectrum sensing is scheduled and spectrum sensors are coordinated for collaborative sensing. Specifically, we propose an efficient MAC-layer sensing scheduling algorithm that discovers spectrum opportunities as much as possible for better quality-of-service (QoS), and as fast as possible for seamless service provisioning. In addition, we propose an optimal in-band spectrum sensing algorithm to protect incumbents by achieving the detectability requirements set by regulators (e.g., FCC) while incurring minimal sensing overhead. For better utilization of discovered spectrum opportunities, we pay our attention to resource allocation in the secondary spectrum market where legacy license holders temporarily lease their own spectrum to secondary wireless service providers (WSPs) for opportunistic spectrum access by CR users. In this setting, we investigate how a secondary WSP can maximize its profit by optimally controlling the admission and eviction of its customers (i.e., CR users). In addition, we also focus on the price and quality competition between co-located WSPs where they contend for enticing customers by providing more competitive service fee while leasing the channels with best matching quality.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78741/1/hyoilkim_1.pd

RiSi: Spectro-temporal RAN-agnostic Modulation Identification for OFDMA Signals

Blind modulation identification is essential for 6G's RAN-agnostic communications, which identifies the modulation type of an incompatible wireless signal without any prior knowledge. Nowadays, research on blind modulation identification relies on deep convolutional networks that deal with a received signal's raw I/Q samples, but they mostly are limited to single-carrier signal recognition thus not pragmatic for identifying spectro-temporal OFDM/OFDMA signals whose modulation varies with time and frequency. Therefore, this paper proposes RiSi, a semantic segmentation neural network designed to work on OFDMA's spectrograms, by replacing vanilla DeepLabV3+'s 2D convolutions with 'flattened' convolutions to enforce the time-frequency orthogonality constraint and to achieve the grid-like pattern of OFDMA's resource blocks, and by introducing three-channel inputs consisting of I/Q/amplitude. Then, we synthesized a realistic and effective dataset consisting of OFDMA signals with various channel impairments to train the proposed network. Moreover, we treated varying communication parameters as different domains to apply domain generalization methods, to enhance our model's adaptability to diverse communication environments. Extensive evaluation shows that RiSi's modulation identification accuracy reaches 86% averaged over four modulation types (BPSK, QPSK, 16-QAM, 64-QAM), while its domain generalization performance for unseen data has been also shown to be reliable.Comment: 10 pages, 10 figure

Flit Scheduling for Cut-through Switching: Towards Near-Zero End-to-end Latency

Achieving low end-to-end latency with high reliability is one of the key objectives for future mission-critical applications, like the Tactile Internet and real-time interactive Virtual/Augmented Reality (VR/AR). To serve the purpose, cut-through (CT) switching is a promising approach to significantly reduce the transmission delay of store-and-forward switching, via flit-ization of a packet and concurrent forwarding of the flits belonging to the same packet. CT switching, however, has been applied only to well-controlled scenarios like network-on-chip and data center networks, and hence flit scheduling in heterogeneous environments (e.g., the Internet and wide area network) has been given little attention. This paper tries to fill the gap to facilitate the adoption of CT switching in the general-purpose data networks. In particular, we first introduce a packet discarding technique that sheds the packet expected to violate its delay requirement and then propose two flit scheduling algorithms, fEDF (flit-based Earliest Deadline First) and fSPF (flit-based Shortest Processing-time First), aiming at enhancing both reliability and end-to-end latency. Considering packet delivery ratio (PDR) as a reliability metric, we performed extensive simulations to show that the proposed scheduling algorithms can enhance PDR by up to 30.11% (when the delay requirement is 7 ms) and the average end-to-end latency by up to 13.86% (when the delay requirement is 10 ms), against first-in first-out (FIFO) scheduling

6G for UAM communications: Challenges and Visions

UAM (Urban Air Mobility) is future aerial mobility for passengers and cargo, usually based on eVTOLs (electric vertical take-off and landing aircrafts). UAM communications is a key enabling technology of UAM systems and services, to ensure efficient and safe navigation and to provide network access to passengers. Traditional terrestrial networks, however, are not reliable to fulfill the requirements of UAM since they have not been designed to support airborne mobile users in the three-dimensional space. In the meantime, 6G communications is considered as a good match to UAM due to its vision to cover ground, air, and space. Hence, this paper overviews major challenges of UAM communications and their existing approaches, and then provides future research directions for 6G-based UAM communications

Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks

We address the problem of rapidly discovering spectrum opportunities for seamless service provisioning for secondary users (SUs) in cognitive radio networks (CRNs). Specifically, we propose an efficient sensing-sequence that incurs a small opportunity-discovery delay by considering (1) the probability that a spectrum band (or a channel) may be available at the time of sensing, (2) the duration of sensing on a channel, and (3) the channel capacity. We derive the optimal sensing-sequence for channels with homogeneous capacities, and a suboptimal sequence for channels with heterogeneous capacities for which the problem of finding the optimal sensing-sequence is shown to be NP-hard. To support the proposed sensing-sequence, we also propose a channel-management strategy that optimally selects and updates the list of backup channels. A hybrid of maximum likelihood (ML) and Bayesian inference is also introduced for flexible estimation of ON/OFF channel-usage patterns and prediction of channel availability when sensing produces infrequent samples. The proposed schemes are evaluated via in-depth simulation. For the scenarios we considered, the proposed suboptimal sequence is shown to achieve close-to-optimal performance, reducing the opportunity-discovery delay by up to 47% over an existing probability-based sequence. The hybrid estimation strategy is also shown to outperform the ML-only strategy by reducing the overall opportunity-discovery delay by up to 34%

Asymmetry-Aware Real-Time Distributed Joint Resource Allocation in IEEE 802.22 WRANs

In IEEE 802.22 Wireless Regional Area Networks (WRANs), each Base Station (BS) solves a complex resource allocation problem of simultaneously determining the channel to reuse, power for adaptive coverage, and Consumer Premise Equipments (CPEs) to associate with, while maximizing the total downstream capacity of CPEs. Although joint power and channel allocation is a classical problem, resource allocation in WRANs faces two unique challenges that has not yet been addressed: (1) the presence of small-scale incumbents such as wireless microphones (WMs), and (2) asymmetric interference patterns between BSs using omnidirectional antennas and CPEs using directional antennas. In this paper, we capture this asymmetry in upstream/downstream communications to propose an accurate and realistic WRAN-WM coexistence model that increases spatial reuse of TV spectrum while protecting small-scale incumbents. Based on the proposed model, we formulate the resource-allocation problem as a mixed-integer nonlinear programming (MINLP) which is NP-hard. To solve the problem in real-time, we propose a suboptimal algorithm based on the Genetic Algorithm (GA), and extend the basic GA algorithm to a fully-distributed GA algorithm (dGA) that distributes computational cost over the network and achieves scalability via local cooperation between neighboring BSs. Using extensive simulation, the proposed dGA is shown to perform as good as 99.4-99.8% of the optimal solution, while reducing the computational cost significantly

QoE-aware Computation Offloading to Capture Energy-Latency-Pricing Tradeoff in Mobile Clouds

Computation offloading in mobile clouds helps mobile users save energy and enhance performance via mobile-to-cloud migration of processing. Although there exist many approaches to computation offloading, they have not explicitly considered the energy-latency-pricing tradeoff from the viewpoint of mobile users' context, e.g., user tendency, the remaining battery level. This paper tries to capture the user-centric perspective via quality-of-experience (QoE), and formulates two important problems: mobile-to-cloud transmission scheduling of the offloaded task's data and offloading service class selection. Regarding transmission scheduling, we introduce a database-assisted optimal dynamic programming (DP) algorithm and then propose two suboptimal but computationally-efficient approximate DP algorithms, ADP and ADPe, based on the limited lookahead technique. Regarding service class selection, we consider multiple service classes with different computing power and service charge, and formulate an optimization problem to minimize the overall cost incurred during offloading. An extensive numerical analysis has revealed that ADP and ADPe achieve near-optimal performance incurring only 0.35% and 2.1% extra cost than the optimum on average, and enhances the QoE-aware cost by up to 2.38 times compared to the energy-only scheduling. In addition, our service class selection algorithm is shown to choose the best class according to user tendency and the remaining battery level