We present DeepRadar, a novel deep-learning-based environmental sensing capability system for detecting radar signals and estimating their spectral occupancy. DeepRadar makes decisions in real-time and maintains continuous operability by adapting its computations based on the available computing resources. We thoroughly evaluate DeepRadar using a variety of test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at 20 dB peak-to-average SIR, per MHz, DeepRadar detects radar signals with 99% accuracy and misses only less than 2 MHz, on average, while estimating their spectral occupancy. Our implementation of DeepRadar using a commercial-off-the-shelf software-defined radio also achieves a similarly high detection accuracy.

Multipath TCP (MPTCP) is a promising protocol that aggregates the bandwidth of mobile client's multiple interfaces. However, currently it is still not widely adopted. A key reason for this slow adoption is that the layer-4 load balancers (LBs) used to scale TCP based services in data centers are not MPTCP-aware and forward the multiple TCP subflows of the same MPTCP connection independently to different backends (BEs). In this paper, we present RomanRoads (RR), an MPTCP-aware layer-4 LB which eliminates this hurdle to widespread MPTCP adoption. Compared with prior proposals, RR is easily deployable because it does not change the service provider's network configuration, makes no modification to ordinary TCP protocol and only minimal modification to the MPTCP connection setup process, and supports the case of multiple LBs. We implement RR in the form of a software LB and validate its correctness and high performance through extensive experiments. RR achieves 100% correctness at steady state, no connection disruption during LB churns, and line-rate throughput for packets from 5-tuple seen before. Moreover, we shed light on the desired properties of an LB-friendly multipath layer-4 protocol to provide guidance for future multipath protocol design.

We make a case for using the emerging blockchain technology for spectrum sharing in Citizen's Broadband Radio Service (CBRS). We show that blockchain can dynamically allocate channels in a transparent and fair manner by achieving distributed channel de-confliction. Furthermore, blockchain helps automate communication between the Spectrum Access Systems (SASs) belonging to competing, non-trusting organizations by providing consensus, while at the same time preserving confidentiality of sensitive information. We implement our CBRS blockchain architecture using the Hyperledger Fabric platform. In order to support high channel request rates, we also develop a novel feature that adaptively pre-processes channel allocation requests at the SAS before sending those to the blockchain network. We evaluate our blockchain solution under different settings and show that it meets the FCC latency requirements. We also analyze the scalability of our system and show that the latency and throughput guarantees can be maintained in scenarios involving multiple organizations.

The paradigm of shared spectrum allows secondary devices to opportunistically access spectrum bands underutilized by primary owners. Recently, the FCC has targeted the sharing of the 3.5 GHz (3550-3700 MHz) federal spectrum with commercial systems such as small cells. The rules require a spectrum access system (SAS) to accommodate three service tiers: 1) incumbent access; 2) priority access (PA); and 3) generalized authorized access (GAA). In this paper, we study the SAS-assisted dynamic channel assignment (CA) for PA and GAA tiers. We introduce the node-channel-pair conflict graph to capture pairwise interference, channel and geographic contiguity constraints, spatially varying channel availability, and coexistence awareness. The proposed graph representation allows us to formulate PA CA and GAA CA with binary conflicts as max-cardinality and max-reward CA, respectively. Approximate solutions can be found by a heuristic-based algorithm that searches for the maximum weighted independent set. We further formulate GAA CA with non-binary conflicts as max-utility CA. We show that the utility function is submodular, and the problem is an instance of matroid-constrained submodular maximization. A local-search-based polynomial-time algorithm is proposed which provides a provable performance guarantee. Extensive simulations using a real-world Wi-Fi hotspot location data set are performed to evaluate the proposed algorithms. Our results have demonstrated the advantages of the proposed graph representation and improved performance of the proposed algorithms over the baseline algorithms.

Control of data delivery latency in wireless mobile networks is an open problem due to the inherently unreliable and stochastic nature of wireless channels. This paper explores how the current best-effort throughput-oriented wireless services could be evolved into latency-sensitive enablers of new mobile applications such as remote 3D graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the basic latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible data re-ordering delays, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse delivery of data over multiple paths with uncorrelated outage likelihoods, and (ii) forward packet protection based on encoding redundancy that enables proactive recovery of lost or intolerably delayed data without end-to-end re-transmissions. Our analysis is based on traces of real-life traffic in live carrier-grade LTE networks.

In this paper, we make a case for future wireless networks that will seamlessly exploit many variable grade spectrum bands. The transformation we envision will be fueled by a fundamental change in the way networks use spectrum. The mix of spectrum options will include existing exclusively licensed and unlicensed bands and new shared spectrum bands where incumbent primary transmitters with interruptible, exclusive access share the band with cooperating (secondary) users. Such bands used in small cell deployments will be key to creating enormous wireless capacity needed to support future traffic demands. The nascent spectrum database technologies will morph into more dynamic spectrum databases and provide essential interference coordination, channel management and monetization. This trend when combined with infrastructure sharing enabled by cloud and SDN technologies will gradually lead to new deployment models. Such network transformation and democratization of spectrum access can fuel innovative business models and new regulatory regimes for wireless networks.

In this paper, we envision a scenario where mobile devices perform at least part-time spectrum sensing in a collaborative fashion under the control of a central server. The goal is to create an adequate `radio environment map' for the `white spaces' that will be useful for spectrum management decisions. We lay out the research challenges, describe a prototype implementation using a DTV receiver dongle interfaced with an Android-based mobile device, and present preliminary performance measurements.

The volume of data in broadband cellular network is growing exponentially. However, studies have indicated the traffic load on the cellular base stations varies significantly over time. This gives an opportunity to accommodate additional traffic with the same network capacity if some of the traffic (e.g., p2p, cloud sync) can be amenable to `delayed scheduling' without hurting the user experience any significantly. In this paper, we study various algorithmic problems that can arise in this context. Using a model where all flows can have certain flexibility in scheduling (via use of a `deadline'), we develop optimal or near-optimal algorithms to determine the minimum network capacity for two different models. We also develop various semi-online and online algorithms for online scheduling of flows, and analyze their performance. In particular, even though the online scheduling problem is shown to be intractable, our proposed semi-online algorithm can schedule flows optimally if aided by historical data and slightly additional network capacity over the optimal. Finally, using flow level traffic traces collected at the core of a commercially operated cellular network, we evaluate the effectiveness of our techniques. Evaluations show that delayed scheduling, when done efficiently (using an offline optimal algorithm), can accommodate the same traffic with much lower network capacity (up to 50% less) with only modest delays. While such an optimal solution needs an offline approach, we demonstrate that online scheduling can be almost equally effective when historical traffic data can be exploited for estimation purposes.

Resource sharing among mobile network operators is a promising way to tackle growing data demand by increasing capacity and reducing costs of network infrastructure deployment and operation. In this work, we evaluate sharing options that range from simple approaches that are feasible in the near-term on traditional infrastructure to complex methods that require specialized/virtualized infrastructure. We build a simulation testbed supporting two geographically overlapped 4G LTE macro cellular networks and model the sharing architecture/process between the network operators. We compare Capacity Sharing (CS) and Spectrum Sharing (SS) on traditional infrastructure and Virtualized Spectrum Sharing (VSS) and Virtualized PRB Sharing (VPS) on virtualized infrastructure under light, moderate and heavy user loading scenarios in collocated and noncollocated E-UTRAN deployment topologies. We also study these sharing options in conservative and aggressive sharing participation modes. Based on simulation results, we conclude that CS, a generalization of traditional roaming, is the best performing and simplest option, SS is least effective and that VSS and VPS perform better than spectrum sharing with added complexity.

We conduct the first detailed measurement analysis of network resource usage and subscriber behavior using a large-scale data set collected inside a nationwide 3G cellular data network. The data set tracks close to a million subscribers over thousands of base stations. We analyze individual subscriber behaviors and observe a significant variation in network usage among subscribers. We characterize subscriber mobility and temporal activity patterns and identify their relation to traffic volume. We then investigate how efficiently radio resources are used by different subscribers as well as by different applications. We also analyze the network traffic from the point of view of the base stations and find significant temporal and spatial variations in different parts of the network, while the aggregated behavior appears predictable. Broadly, our observations deliver important insights into network-wide resource usage. We describe implications in pricing, protocol design and resource and spectrum management.

We present a novel technique for radio transmitter identification based on frequency domain characteristics. Our technique detects the unique features imbued in a signal as it passes through a transmit chain. We are the first to propose the use of discriminatory classifiers based on steady state spectral features. In laboratory experiments, we achieve 97% accuracy at 30 dB SNR and 66% accuracy at OdB SNR based on eight identical universal software radio peripherals (USRP) transmitters. Our technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.

Broadcast operation, which disseminates information network-wide, is very important in multi-hop wireless networks. Due to the broadcast nature of wireless media, not all nodes need to transmit in order for the message to reach every node. Previous work on broadcast support can be classified as probabilistic (each node rebroadcasts a packet with a given probability) or deterministic approaches (nodes pre-select a few neighbors for rebroadcasting). In this paper, we show how network-coding can be applied to a deterministic broadcast approaches, resulting in significant reductions in the number of transmissions in the network. We propose two algorithms, that rely only on local two-hop topology information and makes extensive use of opportunistic listening to reduce the number of transmissions: 1) a simple XOR-based coding algorithm that provides up to 45% gains compared to a non-coding approach and 2) a Reed-Solomon based coding algorithm that determines the optimal coding gain achievable for a coding algorithm that relies only on local information, with gains up to 61% in our simulations. We also show that our coding-based deterministic approach outperforms the coding-based probabilistic approach presented in (C. Fragouli et al, 2006).

The capacity problem in wireless mesh networks can be alleviated by equipping the mesh routers with multiple radios tuned to non-overlapping channels. However, channel assignment presents a challenge because co-located wireless networks are likely to be tuned to the same channels. The resulting increase in interference can adversely affect performance. This paper presents an interference-aware channel assignment algorithm and protocol for multi-radio wireless mesh networks that address this interference problem. The proposed solution intelligently assigns channels to radios to minimize interference within the mesh network and between the mesh network and co-located wireless networks. It utilizes a novel interference estimation technique implemented at each mesh router. An extension to the conflict graph model, the multi-radio conflict graph, is used to model the interference between the routers. We demonstrate our solution’s practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed. We also report on an extensive evaluation via simulations. In a sample multi-radio scenario, our solution yields performance gains in excess of 40% compared to a static assignment of channels.

The new paradigm of dynamic spectrum access (DSA) networks aims to provide opportunistic access to large parts of the underutilized spectrum. The majority of research in this area has focused on free-for-all, uncoordinated access methods common in ad-hoc military applications (Horne, W. 2003; Leaves, P. et al., 2002; Lehr, W. et al., 2002; Schafer, D.J.; To/spl uml/njes, R., 2002). We argue that a simpler pragmatic approach that offers coordinated, spatially aggregated spectrum access via a regional spectrum broker is more attractive in the immediate future. We first introduce two new concepts, coordinated access band (CAB) and statistically multiplexed access (SMA), to the spectrum. We describe their implementation in the new DIMSUMnet (dynamic intelligent management of spectrum for ubiquitous mobile-access network) architecture consisting of four elements: base stations; clients; a radio access network manager (RAN-MAN) that obtains spectrum leases; a per-domain spectrum broker that controls spectrum access. We also discuss in detail various issues in the design of spectrum brokers and spectrum allocation policies and algorithms.

The emerging multiprotocol label switching (MPLS) networks enable network service providers to route bandwidth guaranteed paths between customer sites. This basic label switched path (LSP) routing is often enhanced using restoration routing which sets up alternate LSPs to guarantee uninterrupted connectivity in case network links or nodes along primary path fail. We address the problem of distributed routing of restoration paths, which can be defined as follows: given a request for a bandwidth guaranteed LSP between two nodes, find a primary LSP, and a set of backup LSPs that protect the links along the primary LSP. A routing algorithm that computes these paths must optimize the restoration latency and the amount of bandwidth used. We introduce the concept of "backtracking" to bound the restoration latency. We consider three different cases characterized by a parameter called backtracking distance D: 1) no backtracking (D=0); 2) limited backtracking (D=k); and 3) unlimited backtracking (D=/spl infin/). We use a link cost model that captures bandwidth sharing among links using various types of aggregate link-state information. We first show that joint optimization of primary and backup paths is NP-hard in all cases. We then consider algorithms that compute primary and backup paths in two separate steps. Using link cost metrics that capture bandwidth sharing, we devise heuristics for each case. Our simulation study shows that these algorithms offer a way to tradeoff bandwidth to meet a range of restoration latency requirements.

The third-generation (3G) wide area wireless networks and 802.11 local area wireless networks possess complementary characteristics. 3G networks promise to offer always-on, ubiquitous connectivity with relatively low data rates. 802.11 offers much higher data rates, comparable to wired networks, but can cover only smaller areas, suitable for hot-spot applications in hotels and airports. The performance and flexibility of wireless data services would be dramatically improved if users could seamlessly roam across the two networks. In this paper, we address the problem of integration of these two classes of networks to offer such seamless connectivity. Specifically, we describe two possible integration approaches - namely tight integration and loose integration and advocate the latter as the preferred approach. Our realization of the loose integration approach consists of two components: a new network element called IOTA gateway deployed in 802.11 networks, and a new client software. The IOTA gateway is composed of several software modules, and with cooperation from the client software offers integrated 802.11/3G wireless data services that support seamless intertechnology mobility, Quality of Service (QoS) guarantees and multiprovider roaming agreements. We describe the design and implementation of the IOTA gateway and the client software in detail and present experimental performance results that validate our architectural approach.

The emerging multi-protocol label switching (MPLS) networks enable network service providers to route bandwidth guaranteed paths between customer sites (see Davie, B. and Rekhter, Y., 2000; Awduche, D. et. al., 1999; Sharma, V. et al., 2002; Jamoussi et al., 2002). This basic label switched path (LSP) routing is often enhanced using restoration routing which sets up alternate LSPs to guarantee uninterrupted connectivity in case network links or nodes along the primary path fail. We address the problem of distributed routing of restoration paths, defined as follows: given a request for a bandwidth guaranteed LSP between two nodes, find a primary LSP and a set of backup LSPs that protect the links along the primary LSP. A routing algorithm that computes these paths must optimize the restoration latency and the amount of bandwidth used. We introduce the concept of "backtracking" to bound the restoration latency. We consider three different cases characterized by a parameter called backtracking distance, D: (1) no backtracking (D=0); (2) limited backtracking (D=k); (3) unlimited backtracking (D=/spl infin/). We use a link cost model that captures bandwidth sharing among links using various types of aggregate link state information. We first show that joint optimization of primary and backup paths is NP-hard in all cases. We then consider algorithms that compute primary and backup paths in two separate steps. Using link cost metrics that capture bandwidth sharing, we devise heuristics for each case. Our simulation study shows that these algorithms offer a way to tradeoff bandwidth to meet a range of restoration latency requirements.

In the current Internet, Web content is increasingly being cached closer to the end user to reduce network and Web server load and improve performance. Existing Web caching systems typically cache entire Web documents and attempt to keep them consistent with the origin server. This approach works well for text and images; for bandwidth intensive multimedia data such as audio and video, caching entire documents is not cost effective and does not scale. An alternative approach is to cache parts of the multimedia stream on different caches in the network and coordinate stream playback from these independent caches. From the perspective of the clients, the collection of cooperating distributed caches acts as a single fault tolerant, scalable cache. In this paper, we focus on data placement and replacement techniques for such co-operating distributed caches. Specifically, we propose the following new schemes that work together. 1) A family of distributed layouts, consisting of two layouts, namely RCache and Silo. The RCache layout is a simple, randomized, easy-to-implement layout that distributes constant length segments of a clip among caches and provides modest storage efficiency. The Silo scheme improves upon RCache; it accounts for long term clip popularity and intraclip segment popularity metrics and provides parameters to tune storage efficiency, server load, and playback switch-overs. 2) Rainbow, a local data replacement scheme based on the concept of segment access potential that accurately captures the popularity metrics. 3) Caching Token, a dynamic global data replacement or redistribution scheme that exploits existing data in distributed caches to minimize data distribution overhead. Our schemes optimize storage space, startup latency, server load, network bandwidth usage, and overhead from playback switch-overs. Our analytical and simulation results show that the silo scheme provides three to eight times higher cache hit ratio than a comparable traditional Web caching system that has the same amount of storage space.

Label switched networks have become increasingly attractive to both network providers and customers. By creating aggregate, bandwidth-reserved flows, these networks offer routing flexibility, predictable bandwidth usage, and quality-of-service (QoS) provisioning. This flexibility in routing enables fault-persistent QoS reservations, where connectivity, and allotted bandwidth remains available, even if some links or network nodes fail. The automatic switch-over from a now-defunct path to a new, working path is known as restoration. Restoring bandwidth-guaranteed paths requires allocation of resources on backup paths that will be used in the event of faults. In this paper, we investigate distributed algorithms for routing with backup restoration. Specifically, we propose a new concept of backup load distribution matrix that captures partial network state, greatly reducing the amount of routing information maintained and transmitted while achieving efficient bandwidth usage. We present and simulate two new distributed routing algorithms, which provide significant improvements in rejection rates and provide substantial savings in bandwidth used and call setup time compared to existing algorithms.

The availability of fast network processors and general purpose CPUs has made software implementation of per-packet processing in network elements an attractive option. Given this, a-priori knowledge of performance of software implementations of the well known Layer-4 packet classification will be very useful. We compare the performance of three state-of-the-art packet classification schemes namely, Grid-of-Tries (GOT), Packet Classification Algorithms using Recursive Space-decomposition (PACARS) , and Tuple-Space-Search (TSS), implemented in FreeBSD 3.3 UNIX kernel. We developed two new OS extensions, namely, the Virtual Filter Database (VFD) framework and the new routing socket API to implement these algorithms. We used real-life as well as synthetic rule databases to evaluate their performance. Our key conclusions are: (1) compression of trie data structure that is central to a lot of classification algorithms has limited benefits on general purpose CPUs; (2) static algorithms such as GOT that do not support dynamic updates support very fast search performance of the order of a few microseconds per search and may be adequate for static firewalls; and (3) with medium sized databases, PACARS and TSS schemes provide update times of the order of 100s of microseconds and search performance of the order of 10s of microseconds. These algorithms are adequate for dynamic firewalls, traffic directors, and network monitoring applications in enterprise networks.

The primary goal of the Axon architecture is to support a high-performance data path delivering high network bandwidth directly to applications. The Axon network interface is described from the perspective of its simulation, and in particular and implementation of error and flow control in hardware. Background is provided on the simulation package that has been used to explore these mechanisms, and a brief overview is given of the Axon architecture is implemented by the simulator. The error control mechanism, the hardware design, and the functional and performance simulation results are outlined. The rate control scheme is described, along with its implementation and simulation.>