The 5G Radio Access Network has shifted towards virtualization and disaggregation. This change aims to reduce costs and foster innovation by promoting vendor interoperability and by expanding the ecosystem. In this environment, smaller RAN vendors and open-source projects have emerged, focusing on low-cost, modular stacks. However, challenges such as achieving state-of-the-art performance and accessing data and control knobs hinder their widespread adoption. To address these issues, we propose a middlebox architecture, called RANBooster, that enhances the RAN capabilities without modifying existing network functions, by leveraging the open fronthaul interface. To demonstrate the benefits of the RANBooster framework, we build four reference applications (distributed antenna system, distributed MIMO, RU sharing, realtime physical resource block monitoring), and evaluate them on an enterprise-scale, commercial-grade 5G testbed.

The Open RAN architecture, with disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diversified and multi-vendor RAN ecosystem. However, these same features contribute to increased operational complexity, making it highly challenging to troubleshoot RAN related performance issues and failures. Tackling this challenge requires a dependable, explainable anomaly detection method that Open RAN is currently lacking. To address this problem, we introduce SpotLight, a tailored system archtecture with a distributed deep generative modeling based method running across the edge and cloud. SpotLight takes in a diverse, fine grained stream of metrics from the RAN and the platform, to continually detect and localize anomalies. It introduces a novel multi-stage generative model to detect potential anomalies at the edge using a light-weight algorithm, followed by anomaly confirmation and an explain-ability phase at the cloud, that helps identify the minimal set of KPIs that caused the anomaly. We evaluate SpotLight using the metrics collected from an enterprise-scale 5G Open RAN deployment in an indoor office building. Our results show that compared to a range of baseline methods, SpotLight yields significant gains in accuracy (13% higher F1 score), explain-ability (2.3 -- 4X reduction in the number of reported KPIs) and efficiency (4 -- 7X bandwidth reduction).

The Open RAN architecture, featuring disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diverse, multi-vendor ecosystem. However, these features also increase operational complexity, complicating the troubleshooting of RAN performance issues and failures. Addressing this challenge requires a reliable, explainable anomaly detection method, which Open RAN currently lacks. To address this problem, we have developed SpotLight, a tailored distributed deep learning method running across the edge and cloud. SpotLight continuously detects and localizes anomalies by analyzing a diverse, fine-grained stream of metrics from the RAN and platform. It employs a novel multi-stage generative model to identify potential anomalies at the edge using a lightweight algorithm, followed by anomaly confirmation and an explainability phase in the cloud, which pinpoints the minimal set of KPIs responsible for the anomaly. In this demo, using a carrier-grade indoor Open RAN testbed with configurable anomaly event generation and replay, we highlight (1) the difficulty of troubleshooting problems in Open RAN and (2) accurate, efficient, and explainable online anomaly detection with SpotLight and corresponding visualization in comparison with prior art.

Open RAN is an emerging paradigm in mobile networks where the Radio Access Network (RAN) functions are disaggregated and virtualized on commodity servers. Despite the importance of Open RAN research, existing platforms often lack the fidelity and stability required to address a wide range of research problems. In response to this limitation, we have developed an enterprise-scale Open RAN testbed aimed at conducting state-of-the-art research in key areas that have received limited attention due to the lack of suitable platforms. In this poster, we provide an overview of the testbed we have created and examples of the research it has enabled, with the hope of catalyzing future open RAN research and innovation.

Open RAN, a modular and disaggregated design paradigm for 5G radio access networks (RAN), promises programmability through the RAN Intelligent Controller (RIC). However, due to latency and safety challenges, the telemetry and control provided by the RIC is mainly limited to higher layers and higher time scales (> 10ms), while also relying on predefined service models which are hard to change. We address these issues by proposing Janus, a fully programmable monitoring and control system, specifically designed with the RAN idiosyncrasies in mind, focused on flexibility, efficiency and safety. Janus builds on eBPF to allow third-parties to load arbitrary codelets inline in the RAN functions in a provably safe manner. We extend eBPF with a novel bytecode patching algorithm that enforces codelet runtime thresholds, and a safe way to collect user-defined telemetry. We demonstrate Janus' flexibility and efficiency by building 3 different classes of applications (18 applications in total) and deploying them on a 100MHz 4×4 MIMO 5G cell without affecting the RAN performance.

A key transformation of the Radio Access Network (RAN) in 5G is the migration to an Open RAN architecture, that sees the RAN functions virtualized and disaggregated. Open RAN, aims at accelerating innovation through the introduction of a programmable RAN Intelligent Controller (RIC). However, due to latency and safety challenges, the telemetry and control provided by the RIC is mainly limited to higher layers and time scales (>10ms), while also relying on predefined service models which are hard to change. In this work, we demonstrate Minerva, a programmable monitoring and control platform, specifically designed with the RAN idiosyncrasies in mind. Minerva introduces two novel components called Janus and Decima, for the deployment of safe RAN control and telemetry applications by trusted third-parties. Janus enables the deployment of codelets in the RAN functions using userspace eBPF for fast inline control and filtering of data. Decima, enables the deployment of sandboxed real-time control applications, by leveraging data collected from Janus and the OS. We demonstrate the benefits of Minerva through two applications related to interference detection and inter-cell interference mitigation, by leveraging a commercial-grade 5G testbed deployment.

State-of-the-art distributed in-memory datastores (FaRM, FaSST, DrTM) provide strongly-consistent distributed transactions with high performance and availability. Transactions in those systems are fully general; they can atomically manipulate any set of objects in the store, regardless of their location. To achieve this, these systems use complex distributed transactional protocols. Meanwhile, many workloads have a high degree of locality. For such workloads, distributed transactions are an overkill as most operations only access objects located on the same server - if sharded appropriately.

Virtualized Radio Access Network (vRAN) offers a cost-efficient solution for running the 5G RAN as a virtualized network function (VNF) on commodity hardware. The vRAN is more efficient than traditional RANs, as it multiplexes several base station workloads on the same compute hardware. Our measurements show that, whilst this multiplexing provides efficiency gains, more than 50% of the CPU cycles in typical vRAN settings still remain unused. A way to further improve CPU utilization is to collocate the vRAN with general-purpose workloads. However, to maintain performance, vRAN tasks have sub-millisecond latency requirements that have to be met 99.999% of times. We show that this is difficult to achieve with existing systems. We propose Concordia, a userspace deadline scheduling framework for the vRAN on Linux. Concordia builds prediction models using quantile decision trees to predict the worst case execution times of vRAN signal processing tasks. The Concordia scheduler is fast (runs every 20 us) and the prediction models are accurate, enabling the system to reserve a minimum number of cores required for vRAN tasks, leaving the rest for general-purpose workloads. We evaluate Concordia on a commercial-grade reference vRAN platform. We show that it meets the 99.999% reliability requirements and reclaims more than 70% of idle CPU cycles without affecting the RAN performance.

The citizen broadband radio service (CBRS) is a newly re-purposed spectrum band in 3550-3700 MHz, reclaiming spectrum occasionally used by radars and other incumbents for mobile data communication. It is also a poster child for future LTE-based dynamic spectrum access systems. At present, CBRS does not manage interference from unlicensed LTE users, which we show can be detrimental for its performance. In this paper we develop F-CBRS, a decentralized spectrum interference management system for unlicensed LTE users in the CBRS band. We first look at how much information can each operator be allowed to conceal and how much it has to be mandated (by a regulator) to disclose, and formally prove that the network can achieve fairness only if all operators share fully verifiable information about Access point (AP) locations and user activity. Using this insight we design a channel allocation scheme to efficiently utilize spectrum and incentivise collaboration. This also includes a simple, non-disruptive channel change scheme to frequently and efficiently change channels to accommodate dynamic traffic and environments. Through simulation and testbed evaluation, we show that we increase throughput of more than 90% of the flow by 80%-100% compared to the current CBRS protocol.

Mobile phones and innovative data oriented mobile services have the potential to bridge the digital divide in Internet access and have transformative developmental impact. However as things stand currently, economics come in the way for traditional mobile operators to reach out and provide high-end services to under-served regions. We propose a do-it-yourself (DIY) model for deploying mobile networks in such regions that is in the spirit of earlier community cellular networks but aimed at provisioning high-end (4G and beyond) mobile services. Our proposed model captures and incorporates some of the key trends underlying 5G mobile networks and look to expand their scope beyond urban areas to reach all by empowering small-scale local operators and communities to build and operate modern mobile networks themselves. We showcase a particular instance of the proposed deployment model through a trial deployment in rural UK to demonstrate its practical feasibility.

Economies of scale associated with hyper-scale public cloud platforms offer flexibility and cost-effectiveness, resulting in various services and businesses moving to the cloud. One area with little progress in this direction is cellular core networks. A cellular core network manages the state of cellular clients; it is essentially a large distributed state machine with very different virtualization challenges compared to typical cloud services. In this paper we present a novel cellular core network architecture, called ECHO, particularly suited to public cloud deployments, where the availability guarantees might be an order of magnitude worse compared to existing (redundant) hardware platforms. We present the design and implementation of our approach and evaluate its functionality on a public cloud platform. Analysis shows ECHO promises higher availability than existing telco solutions.

In this paper we study network architecture for unlicensed cellular networking for outdoor coverage in TV white spaces. The main technology proposed for TV white spaces is 802.11af, a Wi-Fi variant adapted for TV frequencies. However, 802.11af is originally designed for improved indoor propagation. We show that long links, typical for outdoor use, exacerbate known Wi-Fi issues, such as hidden and exposed terminal, and significantly reduce its efficiency.

OFDM is currently the most popular PHY-layer carrier modulation technique, used in the latest generations of cellular, Wi-Fi and TV standards. OFDM systems use cycle prefix to mitigate inter-symbol interference. However, most of the existing systems over-provision the size of the cycle prefix considering the worst case scenarios which rarely occur. We propose a novel OFDM PHY receiver design, called CPRecycle , that exploits the redundant cycle prefix to reduce the effects of interference from neighboring nodes. CPRecycle is based on the key observation that the starting position of the FFT window within the cyclic prefix at the OFDM receiver does not affect the received signal but can substantially reduce interference from concurrent transmissions. We further develop an algorithm that is able to find the optimal starting position of the FFT window for each subcarrier using a Gaussian kernel density function and a fixed sphere maximum likelihood detector. Through implementation and extensive evaluations using USRP and off- the-shelf IEEE 802.11g transmitters/interferers, we show the effectiveness of CPRecycle in significantly mitigating interference. CPRecycle only requires local modifications at the receiver and does not require changes in standards, making it incrementally deployable.

Interference in WiFi deployments is a growing problem due to the increasing popularity of WiFi. Therefore it is important that APs find the right channel to operate upon. Through a large scale measurement study involving over 10,000 WiFi APs we show that channel measurements and selection are most effective when performed frequently (every few minutes). This is because of the highly dynamic nature of WiFi traffic congestion. Our key contribution in this paper is a novel approach to distributed channel selection -- Ineffective time Quantum (IQ) Hopping, that is simple enough to be described in three lines and has provable optimality guarantees. IQ-Hopping does not require any explicit channel measurements and can react within a matter of several seconds to bad channel conditions, including microwave ovens, hidden interferers, or dynamically varying congestion. Through implementation and experiments on off-the-shelf WiFi routers (OpenWRT, MadWiFi), we demonstrate the effectiveness of IQ-Hopping.

Software-defined radio (SDR) brings the flexibility of software to wireless protocol design, promising an ideal platform for innovation and rapid protocol deployment. However, implementing modern wireless protocols on existing SDR platforms often requires careful hand-tuning of low-level code, which can undermine the advantages of software. Ziria is a new domain-specific language (DSL) that offers programming abstractions suitable for wireless physical (PHY) layer tasks while emphasizing the pipeline reconfiguration aspects of PHY programming. The Ziria compiler implements a rich set of specialized optimizations, such as lookup table generation and pipeline fusion. We also offer a novel -- due to pipeline reconfiguration -- algorithm to optimize the data widths of computations in Ziria pipelines. We demonstrate the programming flexibility of Ziria and the performance of the generated code through a detailed evaluation of a line-rate Ziria WiFi 802.11a/g implementation that is on par and in many cases outperforms a hand-tuned state-of-the-art C++ implementation on commodity CPUs.

Software-defined radios (SDR) have the potential to bring major innovation in wireless networking design. However, their impact so far has been limited due to complex programming tools. Most of the existing tools are either too slow to achieve the full line speeds of contemporary wireless PHYs or are too complex to master. In this demo we present our novel SDR programming environment called Ziria. Ziria consists of a novel programming language and an optimizing compiler. The compiler is able to synthesize very efficient SDR code from high-level PHY descriptions written in Ziria language. To illustrate its potential, we present the design of an LTE-like PHY layer in Ziria. We run it on the Sora SDR platform and demonstrate on a test-bed that it is able to operate in real-time.

In this paper we consider the communication complexity of approximation algorithms for maximum matching in a graph in the message-passing model of distributed computation. The input graph consists of n vertices and edges partitioned over a set of k sites. The output is an \alpha-approximate maximum matching in the input graph which has to be reported by one of the sites. We show a lower bound on the communication complexity of \Omega(\alpha^2 k n) and show that it is tight up to poly-logarithmic factors. This lower bound also applies to other combinatorial problems on graphs in the message-passing computation model, including max-flow and graph sparsification.

Software-defined radio (SDR) brings the flexibility of software to the domain of wireless protocol design, promising an ideal platform both for research and innovation and rapid deployment of new protocols on existing hardware. However, existing SDR programming platforms require either careful hand-tuning of low-level code, negating many of the advantages of software, or are too slow to be useful in the real world.

Software-defined radios (SDR) have the potential to bring major innovation in wireless networking design. However, their impact so far has been limited due to complex programming tools. Most of the existing tools are either too slow to achieve the full line speeds of contemporary wireless PHYs or are too complex to master. In this demo we present our novel SDR programming environment called Ziria. Ziria consists of a novel programming language and an optimizing compiler. The compiler is able to synthesize very efficient SDR code from high-level PHY descriptions written in Ziria language. To illustrate its potential, we present the design of an LTE-like PHY layer in Ziria. We run it on the Sora SDR platform and demonstrate on a test-bed that it is able to operate in real-time.

Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the streaming setting are in wide use: place the newly arrived vertex in the cluster with the largest number of neighbors or in the cluster with the least number of non-neighbors.

Wireless performance depends directly on the quality of the channel. A wireless transmitter can improve its performance by estimating and transmitting on only the strongest channel, which can be of significantly higher quality than a weak channel (yielding up to 100% rate improvement). It is considered impossible to predict the quality of the unseen channels. Thus, the only way to identify the strongest channel is by probing each channel individually, incurring large over- heads. The key contribution of this paper is a discovery of previously unobserved properties of the wireless channel that makes it possible to predict the the strongest of a set of channels from the measurements collected only on a single channel. We confirm the properties through measurements and present a theoretical analysis that explains their nature. Our proposed system, CSpy, utilizes these observations to predict the strongest channel. CSpy is the first to reliably estimate the strongest channel by utilizing channel responses extracted from off-the-shelf wireless chipsets, without probing any additional channels. By tracking the strongest channel, CSpy improves performance by up to 100% in comparison to channel agnostic schemes.

We consider the downlink of a cellular network supporting data traffic in which each user is equipped with the same type of IEEE 802.11-like WLAN or WPAN interface used to relay packets to further users. We are interested in the design guidelines for such networks and how much capacity improvements the additional relay layer can bring. A first objective is to provide a scheduling/relay strategy that maximizes the network capacity. Using theoretical analysis, numerical evaluation, and simulations, we find that when the number of active users is large, the capacity-achieving strategy divides the cell into two areas: one closer to the base station where the relay layer is always saturated and some nodes receive traffic through both direct and relay links, and the farther one where the relay is never saturated and the direct traffic is almost nonexistent. We also show that it is approximately optimal to use fixed relay link lengths, and we derive this length. We show that the obtained capacity is independent of the cell size (unlike in traditional cellular networks). Based on our findings, we propose simple decentralized routing and scheduling protocols. We show that in a fully saturated network our optimized protocol substantially improves performance over the protocols that use naive relay-only or direct-only policies.

One of the key distinctive requirements of white-space networks is the power asymmetry. Static nodes are allowed to transmit with 15dB-20dB higher power than mobile nodes. This poses significant coexistence problems, as high-power nodes can easily starve low-power nodes. In this paper, we propose Weeble, a novel distributed and state-less MAC protocol that solves the coexistence problem. One of the key building blocks is an adaptive preamble support, an add-on to the PHY layer that allows high-power nodes to detect a low-power transmission even when the difference in transmit power is as high as 20dB. The other key building block is a MAC protocol that exploits the adaptive preambles functionality. It implements a virtual carrier-sensing and automatically adapts the preamble size to optimize network performance. We extensively evaluate our system in a test-bed and in simulations. We show that we can prevent starvation of low-power nodes in almost all existing scenarios and improve the data rates of low-power links several-fold over existing MACs, and as a trade-off we decrease the throughput of the rest of the system by 20%-40%.

This paper explores the viability of precise indoor localization using physical layer information in WiFi systems. We find evidence that channel responses from multiple OFDM subcarriers can be a promising location signature. While these signatures certainly vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We attempt to harness these opportunities through a functional system called PinLoc, implemented on off-the-shelf Intel 5300 cards. We evaluate the system in a busy engineering building, a crowded student center, a cafeteria, and at the Duke University museum, and demonstrate localization accuracies in the granularity of 1m x 1m boxes, called "spots". Results from 100 spots show that PinLoc is able to localize users to the correct spot with 89% mean accuracy, while incurring less than 6% false positives. We believe this is an important step forward, compared to the best indoor localization schemes of today, such as Horus.

We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to k sites, and the sites are required to communicate with each other via the coordinator. At each time-step t, one of the k site nodes has to pick an expert from the set {1, . . . , n}, and the same site receives information about payoffs of all experts for that round. The goal of the distributed system is to minimize regret at time horizon T, while simultaneously keeping communication to a minimum. The two extreme solutions to this problem are: (i) Full communication: This essentially simulates the non-distributed setting to obtain the optimal O(\sqrt{log(n)T}) regret bound at the cost of T communication. (ii) No communication: Each site runs an independent copy – the regret is O(\sqrt{log(n)kT}) and the communication is 0. This paper shows the difficulty of simultaneously achieving regret asymptotically better than \sqrt{kT} and communication better than T. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret O(\sqrt{k^{5(1+\epsilon)/6} T}) and communication O(T/k^\epsilon), for any value of \epsilon in (0, 1/5). We also consider a variant of the model, where the coordinator picks the expert. In this model, we show that the label-efficient forecaster of Cesa-Bianchi et al. (2005) already gives us strategy that is near optimal in regret vs communication trade-off.

We consider the continual count tracking problem in a distributed environment where the input is an aggregate stream that originates from k distinct sites and the updates are allowed to be non-monotonic, i.e. both increments and decrements are allowed. The goal is to continually track the count within a prescribed relative accuracy ε at the lowest possible communication cost. Specifically, we consider an adversarial setting where the input values are selected and assigned to sites by an adversary but the order is according to a random permutation or is a random i.i.d process. The input stream of values is allowed to be non-monotonic with an unknown drift -1≤μ=1 where the case μ = 1 corresponds to the special case of a monotonic stream of only non-negative updates. We show that a randomized algorithm guarantees to track the count accurately with high probability and has the expected communication cost Õ(min√k/(|#956;|ε), √k n/ε, n}), for an input stream of length n, and establish matching lower bounds. This improves upon previously best known algorithm whose expected communication cost is Θ(min√k/ε,n]) that applies only to an important but more restrictive class of monotonic input streams, and our results are substantially more positive than the communication complexity of Ω(n) under fully adversarial input. We also show how our framework can also accommodate other types of random input streams, including fractional Brownian motion that has been widely used to model temporal long-range dependencies observed in many natural phenomena. Last but not least, we show how our non-monotonic counter can be applied to track the second frequency moment and to a Bayesian linear regression problem.

We investigate dynamic channel, rate selection and scheduling for wireless systems which exploit the large number of channels available in the White-space spectrum. We first present measurements of radio channel characteristics from an indoor testbed operating in the 500 to 600MHz band and comprising 11 channels. We observe significant and unpredictable (non-stationary) variations in the quality of these channels, and demonstrate the potential benefit in throughput from tracking the best channel and also from optimally adapting the transmission rate. We propose adaptive learning schemes able to efficiently track the best channel and rate for transmission, even in scenarios with non-stationary channel condition variations. We also describe a joint scheduling scheme for providing fairness in an Access Point scenario. Finally, we implement the proposed adaptive scheme in our testbed, and demonstrate that it achieves significant throughput improvement (typically from 40% to 100%) compared to traditional fixed channel selection schemes.

This paper shows the viability of precise indoor localization using physical layer information in WiFi systems. We find that channel frequency responses across multiple OFDM sub-carriers can be suitably aggregated into a location fingerprint. While these fingerprints vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We demonstrate these ideas through a functional prototype, implemented on off-the-shelf Intel 5300 cards (that export per-subcarrier information to the driver). We evaluate the prototype using the existing APs inside a busy building, a cafeteria, and a museum, and demonstrate localization accuracies in the granularity of 1m × 1m boxes, called spots. Results show that our system, PinLoc, is able to localize users to a spot with 90% mean accuracy, while incurring less than 6% false positives. We believe this holds promise towards an important development in indoor localization.

The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.

This poster shows the viability of precise indoor localization using physical layer information in WiFi systems. We find that channel frequency responses across multiple OFDM subcarriers can be suitably aggregated into a location signature. While these signatures vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We demonstrate these ideas through a functional system, implemented on off-the-shelf Intel 5300 cards (that are designed to export per-subcarrier information to the driver). We evaluate the system in a busy engineering building, a cafeteria, and the university museum, and demonstrate localization accuracies in the granularity of 1m x 1m boxes, called spots. Results show that our system, PinLoc, is able to localize users to a spot with 90% mean accuracy, while incurring less than 6% false positives. We believe this is an important step forward, compared to the best indoor localization schemes of today.

Recent advances in PHY layer design demonstrated efficient self-interference cancellation and full-duplex in a single band. Building a MAC that exploits self-interference cancellation is a challenging task. Links can be scheduled concurrently, but only if they either (i) don't interfere or (ii) allow for self-interference cancellation. Two issues arise: Firstly, it is difficult to construct a schedule that fully exploits the potentials for self-interference cancellation for arbitrary traffic patterns. Secondly, designing an efficient and fair distributed MAC is a daunting task; the issues become even more pronounced when scheduling under the constraints. We propose ContraFlow, a novel MAC that exploits the benefits of self-interference cancellation and increases spatial reuse. We use full-duplex to eliminate hidden terminals, and we rectify decentralized coordination inefficiencies among nodes, thereby improving fairness. Using measurements and simulations we illustrate the performance gains achieved when ContraFlow is used and we obtain both a throughput increase over current systems, as well as a significant improvement in fairness.

In Wireless LANs, users may adapt their transmission rates depending on the radio conditions of their links so as to maximize their throughput. Recently, there has been a significant research effort in developing distributed rate adaptation schemes. Unlike previous works that mainly focus on channel tracking, this paper characterizes the optimal reaction of a rate adaptation protocol to the contention information received from the MAC. We formulate this problem analytically. We study both competitive and cooperative user behaviors. In the case of competition, users selfishly adapt their rates so as to maximize their own throughput, whereas in the case of cooperation they adapt their rates so as to maximize the overall system throughput. We show that the Nash Equilibrium reached in the case of competition is inefficient (i.e. the price of anarchy goes to infinity as the number of users increases), and provide insightful properties of the socially optimal rate adaptation schemes. We find that recently proposed collision-aware rate adaptation algorithms decrease the price of anarchy. We also propose a novel collision-aware rate adaptation algorithm that further reduces the price of anarchy.

We consider the problem of traffic management in small networks with both wireless and wired devices, connected to the Internet through a single gateway. Examples of such networks are small office networks or residential networks, where typically traffic management is limited to flow prioritization through port-based filtering.

We consider a hybrid wireless sensor network with regular and transmit-only sensors. The transmit-only sensors do not have the receiver circuit (or have a very low data-rate one), hence are cheaper and less energy consuming, but their transmissions cannot be coordinated. Regular sensors, also called cluster-heads, are responsible for receiving information from the transmit-only sensors and forwarding it to sinks. The main goal of such a hybrid network is to reduce the cost of deployment while achieving some performance goals (minimum coverage, sensing rate, etc). In this paper we are interested in the communication between the transmit-only sensors and the cluster-heads. Since the sensors have no feedback, their transmission schedule is random. The cluster-heads, on the contrary, adapt their reception policy to achieve the performance goals. Using a mathematical model of random access networks developed in [1] we define and evaluate packet admission policies for different performance criteria. We show that the proposed hybrid network architecture, using the optimal policies, can achieve substantial dollar cost and power consumption savings as compared to conventional architectures while providing the same performance guarantees.

We consider wireless mesh networks, and exploit the inherent broadcast nature of wireless by making use of multipath routing. We present an optimization framework that enables us to derive optimal flow control, routing, scheduling, and rate adaptation schemes, where we use network coding to ease the routing problem. We prove optimality and derive a primal-dual algorithm that lays the basis for a practical protocol. We use simulation to show on realistic topologies that we can achieve 20-200% throughput improvement compared to single path routing, and several times compared to a recent related opportunistic protocol (MORE).

There has been extensive work on network architectures that support multi-path routing to improve performance in wireless mesh networks. However, previous work uses ad-hoc design principles that cannot guarantee any network-wide performance objectives such as conjointly maximizing resource utilization and improving fairness. In parallel, numerous theoretical results have addressed the issue of optimizing a combined metric of network utilization and fairness using techniques based on back-pressure scheduling, routing and flow control. However, the proposed theoretical algorithms are extremely difficult to implement in practice, especially in the presence of the 802.11 MAC and TCP. We propose Horizon, a novel system design for multi-path forwarding in wireless meshes, based on the theoretical results on back-pressure. Our design works with an unmodified TCP stack and on top of the existing 802.11 MAC. We modified the back-pressure approach to obtain a simple 802.11-compatible packet-forwarding heuristic and a novel, light-weight path estimator, while maintaining global optimality properties. We propose a delayed reordering algorithm that eliminates TCP timeouts while keeping TCP packet reordering to a minimum. We have evaluated our implementation on a 22-node testbed. We have shown that Horizon effectively utilizes available resources (disjoint paths). In contrast to previous work, our design not only avoids bottlenecks but also optimally load-balances traffic across them when needed, improving fairness among competing flows. To our knowledge, Horizon is the first practical wireless system based on back-pressure.

Designing high throughput wireless mesh networks involves solving interrelated scheduling, routing, and interference problems. In this paper, we exploit the broadcast properties and the path diversity of wireless meshes to implement an efficient multipath routing protocol, Multipath Code Casting (MC2).

We consider the design of the MAC layer for low power, low data-rate, and impulse-radio ultra-wide band (IR-UWB) networks. In such networks, the primary concern is energy consumption rather than rate efficiency. We explore several dimensions such as power control, rate adaptation, mutual exclusion, slotted versus non-slotted operation, power saving modes and interference mitigation. We analyze the effect of these design choices on the energy consumption and rate efficiency. We use a method of energy quanta for computing the energy consumption. We find that for both cases, the optimal operation is non-coordinated and with no power control. Sources should send at their maximum power and not pay attention to neighboring nodes. However, sources should constantly adapt their transmission rate to the level of interference

We consider the design of optimal strategies for joint power adaptation, rate adaptation and scheduling in a multi-hop wireless network. Most existing strategies control either power and scheduling, or rates and scheduling, but not all three together as we do. We assume the underlying physical layer is in the linear regime (the rate of a link can be approximated by a linear function of the signal-to-interference-and-noise ratio), as in time hopping UWB (TH-UWB) and low gain CDMA systems, and that it allows fine-grained rate adaptation, as in 802.11a/g, HDR/CDMA, TH-UWB. The goal is to find properties of the power control in an optimal joint design. Our main finding is that optimal power control is simple 0-P/sup MAX/ power control, i.e. when a node is sending it uses the maximum transmitting power allowed. We consider both high rate networks, where the goal is to maximize rates under power constraints, and low power networks, where the goal is to minimize average consumed power while meeting minimum rate constraints. We prove analytically that, in both scenarios, the optimal can always be attained with 0-P/sup MAX/ power allocation. Moreover, we prove that, when maximizing rates, and if power constraints are on peak and not average, 0-P/sup MAX/ is the only optimal power control strategy, and any other is strictly suboptimal.

Abstract Due to environmental concerns and strict constraints on interference imposed on other networks, the radiated power of emerging pervasive wireless networks needs to be strictly limited, yet without sacrificing acceptable data rates. Pulsed time‐hopping ultra‐wideband (TH‐UWB) is a radio technology that has the potential to satisfy this requirement. Although TH‐UWB is a multi‐user radio technology, non‐zero cross‐correlation between TH sequences, time‐asynchronicity between sources and a multipath channel environment make it sensitive to strong interferers and near‐far scenarios. While most protocols manage interference and multiple‐access through power control or mutual exclusion, we base our design on rate control, a relatively unexplored dimension for multiple‐access and interference management. We further take an advantage of the nature of pulsed TH‐UWB to propose an interference mitigation scheme that alleviates the need for an exclusion scheme. A source is always allowed to send and continuously adapts its channel code (hence its rate) to the interference experienced at the destination. In contrast to power control or exclusion, our MAC layer is local to sender and receiver, and does not need coordination among neighbors not involved in the transmission. We show by simulation that we achieve a significant increase in the network throughput compared to alternative designs. Copyright © 2005 John Wiley & Sons, Ltd.

We present a joint PHY/MAC architecture (DCC-MAC) for 802.15.4a-like networks based on PPM-UWB. Unlike traditional approaches, it fully utilizes the specific nature of UWB to achieve high rates at low protocol complexity. It is the first MAC protocol that adapts the channel code (and thus the bit rate) to interference from concurrent transmissions instead of enforcing exclusion. In order to avoid a complex mutual exclusion protocol at the MAC layer, we propose an interference mitigation scheme. The scheme is based on a modification of the physical layer that cancels much of the interfering energy, in particular from nearby interferers. We further use dynamic channel coding to combat the remaining interference. Sources constantly adjust their channel codes to the level of interference and send incremental redundancy as required. Contention between sources sending to the same destination is solved by a "private MAC" protocol that involves only the nodes that want to talk to the same destination. The private MAC does not use any common channel; this avoids the issues of hidden and exposed terminals altogether. We show by simulation that our MAC protocol fully satisfies the application requirements of 802.15.4a in terms of link lengths, rates and mobility. We further show that it achieves a significant increase in network throughput, compared to traditional MAC protocols like 802.15.4, that are separated from the physical layer.

We consider the maximization when designing ad-hoc wireless network protocols such as routing or MAC. We focus on maximizing rates under battery lifetime and power constraints. Commonly used metrics are total capacity (in the case of cellular networks) and transport capacity (in the case of ad-hoc networks). We review this issue for wireless ad-hoc networks. The story is different for max-min fairness. We show that, in the limit of long battery lifetime, the max-min allocation of rates always leads to strictly equal rates, regardless of the MAC layer, network topology, choice of routes and power constraints. This is due to the "solidarity" property of the set of feasible rates. This results in all flows receiving the rate of the worst flow, and leads to severe inefficiency. We show numerically that the problem persists when battery lifetime constraints are finite. This generalizes the observation reported in the literature that, in heterogeneous settings, 802.11 allocates the worst rate to all stations, and shows that this is inherent to any protocol that implements max-min fairness. Proportional fairness is an alternative to max-min fairness that approximates rate allocation performed by TCP in the Internet. We show by numerical simulations that proportional fairness of rates or transport rates is robust and achieves a good trade-off between efficiency and fairness, unlike total rate or maximum fairness. We thus recommend that metrics for the rate performance of mobile ad-hoc networking protocols be based on proportional fairness.

Ultra-wideband (UWB) is an emerging wireless physical layer technology that uses a very large bandwidth. We are interested in finding the design objectives of the medium access [medium-access control (MAC), namely, power control and scheduling] and routing protocols of a multihop, best-effort, UWB network. Our objective is to maximize flow rates (more precisely, log-utility of flow rates) given node power constraints. The specificity of UWB is expressed by the linear dependence between rate and signal-to-noise ratio at the receiver. It is known that, in wireless networks, different routing strategies can imply differences in MAC protocol design. Hence, we search for the jointly optimal routing, scheduling and power control. We find that the optimal solution is characterized by the following. 1) When data is being sent over a link, it is optimal to have an exclusion region around the destination, in which all nodes remain silent during transmission, whereas nodes outside of this region can transmit in parallel, regardless of the interference they produce at the destination. Additionally, the source adapts its transmission rate according to the level of interference at the destination due to sources outside of the exclusion region. 2) The optimal size of this exclusion region depends only on the transmission power of the source of the link, and not on the length of the link nor on positions of nodes in its vicinity. 3) Each node in a given time slot either sends data at the maximum power, or does not send at all. As for the routing, we restrict ourselves to a subset of routes where on each successive hop we decrease the distance toward the destination, and we show that 4) relaying along a minimum energy and loss route is always better than using longer hops or sending directly, which is not obvious since we optimize rate and not power consumption. Finally 5), the design of the optimal MAC protocol is independent of the choice of the routing protocol. For narrowband networks, 2), 4), and 5) do not hold, which shows that the design of an UWB network should be addressed in a different way than for narrowband. Our technical approach is based on expressing the design requirements as a mathematical optimization problem. We solve it exactly for simple networks on a line and approximately on random topologies in a plane with up to 50 nodes with various power constraints, traffic matrices, and mobility parameters.

We consider the question of what performance metric to maximize when designing ad hoc wireless network protocols such as routing or MAC. We focus on maximizing rates under battery-lifetime and power constraints. Commonly used metrics are total capacity (in the case of cellular networks) and transport capacity (in the case of ad hoc networks). However, it is known in traditional wired networking that maximizing total capacity conflicts with fairness, and this is why fairness-oriented rate allocations, such as max-min fairness, are often used. We review this issue for wireless ad hoc networks. Indeed, the mathematical model for wireless networks has a specificity that makes some of the findings different. It has been reported in the literature on ultra wide band that gross unfairness occurs when maximizing total capacity or transport capacity, and we confirm by a theoretical analysis that this is a fundamental shortcoming of these metrics in wireless ad hoc networks, as it is for wired networks. The story is different for max-min fairness. Although it is perfectly viable for a wired network, it is much less so in our setting. We show that, in the limit of long battery lifetimes, the max-min allocation of rates always leads to strictly equal rates, regardless of the MAC layer, network topology, channel variations, or choice of routes and power constraints. This is due to the "solidarity" property of the set of feasible rates. This results in all flows receiving the rate of the worst flow, and leads to severe inefficiency. We show numerically that the problem persists when battery-lifetime constraints are finite. This generalizes the observation reported in the literature that, in heterogeneous settings, 802.11 allocates the worst rate to all stations, and shows that this is inherent to any protocol that implements max-min fairness. Utility fairness is an alternative to max-min fairness, which approximates rate allocation performed by TCP in the Internet. We analyze by numerical simulations different utility functions and we show that the proportional fairness of rates or transport rates, a particular instance of utility-based metrics, is robust and achieves a good tradeoff between efficiency and fairness, unlike total rate or maximum fairness. We thus recommend that metrics for the rate performance of mobile ad hoc networking protocols be based on proportional fairness.