In this paper, we revisit the idea to remove Slow Start from congestion control. To do so, we build upon the newly gained freedom of transport protocol extendability offered by QUIC to hint bandwidth estimates from a typical web client to a server. Using this bandwidth estimate, we bootstrap congestion windows of new connections to quickly utilize available bandwidth. This custom flow initialization removes the common early exit of Slow Start and thus fuels short flow fairness with long-running connections. Our results indicate that we can drastically reduce flow completion time accepting some losses and thereby an inflated transmission volume. For example, for a typical DSL client, loading a 2 MB YouTube video chunk is accelerated by nearly 2x. In the worst case, we find an inflation of the transfer volume by 12 due to losses.
While past connections can hint at receiver network conditions, we rather see them as a building block that can be used in combination with a receiver-side bandwidth hint. In our work, we highlighted possible angles in which to tackle bandwidth estimates but do not go into detail. @cite_7 shows how to utilize cellular (4G) PHY-layer information at the client for bandwidth estimations, they even implement it in the Google QUIC framework but do not show the effects on other flows, fairness and losses nor focus on the startup phase. Further bandwidth estimations @cite_1 @cite_24 utilize client context information ( location, signal quality, speed) in cellular networks to learn and predict channel throughput, which could be used in an implementation of our system.
@cite_24 @cite_1 @cite_7
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We investigate a wireless system of multiple cells, each having a downlink shared channel in support of high-speed packet data services. In practice, such a system consists of hierarchically organized entities including a central server, Base Stations (BSs), and Mobile Stations (MSs). Our goal is to improve global resource utilization and reduce regional congestion given asymmetric arrivals and departures of mobile users, a goal requiring load balancing among multiple cells. For this purpose, we propose a scalable cross-layer framework to coordinate packet-level scheduling, call-level cell-site selection and handoff, and system-level cell coverage based on load, throughput, and channel measurements. In this framework, an opportunistic scheduling algorithm--the weighted Alpha-Rule--exploits the gain of multiuser diversity in each cell independently, trading aggregate (mean) down-link throughput for fairness and minimum rate guarantees among MSs. Each MS adapts to its channel dynamics and the load fluctuations in neighboring cells, in accordance with MSs' mobility or their arrival and departure, by initiating load-aware handoff and cell-site selection. The central server adjusts schedulers of all cells to coordinate their coverage by prompting cell breathing or distributed MS handoffs. Across the whole system, BSs and MSs constantly monitor their load, throughput, or channel quality in order to facilitate the overall system coordination. Our specific contributions in such a framework are highlighted by the minimum-rate guaranteed weighted Alpha-Rule scheduling, the load-aware MS handoff cell-site selection, and the Media Access Control (MAC)-layer cell breathing. Our evaluations show that the proposed framework can improve global resource utilization and load balancing, resulting in a smaller blocking rate of MS arrivals without extra resources while the aggregate throughput remains roughly the same or improved at the hot-spots. Our simulation tests also show that the coordinated system is robust to dynamic load fluctuations and is scalable to both the system dimension and the size of MS population. How to keep the probability of hand-off drops within a prespecified limit is a very important quality-of-service (QoS) issue in cellular networks because mobile users should be able to maintain ongoing sessions even during their hand-off from one cell to another. We design and evaluate predictive and adaptive schemes for bandwidth reservation for the hand-offs of ongoing sessions and the admission control of new connections. We first develop a method to estimate user mobility based on an aggregate history of hand-offs observed in each cell. This method is then used to probabilistically predict mobiles' directions and hand-off times in a cell. For each cell, the bandwidth to be reserved for hand-offs is calculated by estimating the total sum of tractional bandwidths of the expected hand-offs within a mobility-estimation time window. Three different admission-control schemes for new connection requests using this bandwidth reservation are proposed. We also consider variations that utilize the path location information available from the car navigation system or global positioning system. Finally, we evaluate the performance of the proposed schemes extensively to show that they meet our design goal and outperform the static reservation scheme under various scenarios. Next-generation cellular networks will provide higher cell capacity by adopting advanced physical layer techniques and broader bandwidth. Even in such networks, boundary users would suffer from low throughput due to severe intercell interference and unbalanced user distributions among cells, unless additional schemes to mitigate this problem are employed. In this paper, we tackle this problem by jointly optimizing partial frequency reuse and load-balancing schemes in a multicell network. We formulate this problem as a network-wide utility maximization problem and propose optimal offline and practical online algorithms to solve this. Our online algorithm turns out to be a simple mixture of inter- and intra-cell handover mechanisms for existing users and user association control and cell-site selection mechanisms for newly arriving users. A remarkable feature of the proposed algorithm is that it uses a notion of expected throughput as the decision making metric, as opposed to signal strength in conventional systems. Extensive simulations demonstrate that our online algorithm can not only closely approximate network-wide proportional fairness but also provide two types of gain, interference avoidance gain and load balancing gain, which yield 20 100 throughput improvement of boundary users (depending on traffic load distribution), while not penalizing total system throughput.We also demonstrate that this improvement cannot be achieved by conventional systems using universal frequency reuse and signal strength as the decision making metric. In cellular networks, an important practical issue is how to limit the handoff dropping probability efficiently. One possible approach is to perform dynamic bandwidth reservation based on mobility predictions. With the rapid advances in mobile positioning technology, and the widespread availability of digital road maps previously designed for navigational devices, we propose a predictive bandwidth reservation scheme built upon these timely opportunities. In contrast to the common practice of utilizing only incoming handoff predictions at each cell to compute the reservations, our scheme is more efficient as it innovatively utilizes both incoming and outgoing handoff predictions; it can meet the same target handoff dropping probability by blocking fewer new calls. The individual base stations are responsible for the computations, which are shown to be simple enough to be performed in real-time. We evaluate the scheme via simulation, along with five other schemes for comparison. Simulation results show that those schemes that rely on positioning information are significantly more efficient than those that do not. Our scheme's additional use of the road topology information further improves upon this advantage, bringing the efficiency closer to the bound set by a benchmark scheme that assumes perfect knowledge about future handoffs. Recently, the capacity region of a multiple-input multiple-output (MIMO) Gaussian broadcast channel, with Gaussian codebooks and known-interference cancellation through dirty paper coding, was shown to equal the union of the capacity regions of a collection of MIMO multiple-access channels. We use this duality result to evaluate the system capacity achievable in a cellular wireless network with multiple antennas at the base station and multiple antennas at each terminal. Some fundamental properties of the rate region are exhibited and algorithms for determining the optimal weighted rate sum and the optimal covariance matrices for achieving a given rate vector on the boundary of the rate region are presented. These algorithms are then used in a simulation study to determine potential capacity enhancements to a cellular system through known-interference cancellation. We study both the circuit data scenario in which each user requires a constant data rate in every frame and the packet data scenario in which users can be assigned a variable rate in each frame so as to maximize the long-term average throughput. In the case of circuit data, the outage probability as a function of the number of active users served at a given rate is determined through simulations. For the packet data case, long-term average throughputs that can be achieved using the proportionally fair scheduling algorithm are determined. We generalize the zero-forcing beamforming technique to the multiple receive antennas case and use this as the baseline for the packet data throughput evaluation. With the advent of high-speed cellular access and the overwhelming popularity of smartphones, a large percent of today's Internet content is being delivered via cellular links. Due to the nature of long-range wireless signal propagation, the capacity of the last hop cellular link can vary by orders of magnitude within a short period of time (e.g., a few seconds). Unfortunately, TCP does not perform well in such fast-changing environments, potentially leading to poor spectrum utilization and high end-to-end packet delay. In this paper we revisit seminal work in cross-layer optimization in the context of 4G cellular networks. Specifically, we leverage the rich physical layer information exchanged between base stations (NodeB) and mobile phones (UE) to predict the capacity of the underlying cellular link, and propose nCQIC, a cross-layer congestion control design. Experiments on real cellular networks confirm that our capacity estimation method is both accurate and precise. A CQIC sender uses these capacity estimates to adjust its packet sending behavior. Our preliminary evaluation reveals that CQIC improves throughput over TCP by 1.08-2.89x for small and medium flows. For large flows, CQIC attains throughput comparable to TCP while reducing the average RTT by 2.38-2.65x. Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate. Future 5G cellular networks supporting delay-sensitive, low-latency communications could employ random access communication to reduce the overhead compared to scheduled access techniques used in 4G networks. We consider a wireless communication system where multiple devices transmit payloads of a given fixed size in a random access fashion over shared radio resources to a common receiver. We allow retransmissions and assume Chase combining at the receiver. The radio resources are partitioned in the time and frequency dimensions, and we determine the optimal partition granularity to maximize throughput, subject to given constraints on latency and outage. In the regime of high and low signal-to-noise ratio (SNR), we derive explicit expressions for the granularity and throughput, first using a Shannon capacity approximation and then using finite block length analysis. Numerical results show that the throughput scaling results are applicable over a range of SNRs. The proposed analytical framework can provide insights for resource allocation strategies in reliable and delay-sensitive random access systems and in specific 5G use cases for massive, short packet uplink access. In CSMA CA-based, multi-hop, multi-rate wireless networks, spatial reuse can be increased by tuning the carrier-sensing threshold (Tcs) to reduce the carrier sense range (dcs). While reducing dcs enables more concurrent transmissions, the transmission quality suffers from the increased accumulative interference contributed by concurrent transmissions outside dcs. As a result, the data rate at which the transmission can sustain may decrease. How to balance the interplay of spatial reuse and transmission quality (and hence the sustainable data rate) so as to achieve high network capacity is thus an important issue. In this paper, we investigate this issue by extending Cali's model and devising an analytical model that characterizes the transmission activities as governed by IEEE 802.11 DCF in a single-channel, multi-rate, multi-hop wireless network. The systems throughput is derived as a function of Tcs, SINR, beta, and other PHY MAC systems parameters. We incorporate the effect of varying the degree of spatial reuse by tuning the Tcs. Based on the physical radio propagation model, we theoretically estimate the potential accumulated interference contributed by concurrent transmissions and the corresponding SINR. For a given SINR value, we then determine an appropriate data rate at which a transmission can sustain. To the best of our knowledge, this is perhaps the first effort that considers tuning of PHY characteristics (transmit power and data rates) and MAC parameters (contention backoff timer) jointly in an unified framework in order to optimize the overall network throughput. Analytical results indicate that the systems throughput is not a monotonically increasing decreasing function of Tcs, but instead exhibits transitional points where several possible choices of Tcs can be made. In addition, the network capacity can be further improved by choosing the backoff timer values appropriately. In this paper we propose the following approach to the dimensioning of the radio part of the downlink in OFDMA networks. First, we use information theory to characterize the bit-rate in the channel from a base station to its mobile. It depends on the power and bandwidth allocated to this mobile. Then, we describe the resource (power and bandwidth) allocation problem and characterise feasible configurations of bit-rates of all users. As the key element, we propose some particular sufficient condition (in a multi-Erlang form) for a given configuration of bit-rates to be feasible. Finally, we consider an Erlang's loss model, in which streaming arrivals whose admission would lead to the violation of this sufficient condition are blocked and lost. In this model, the blocking probabilities can be calculated using Kaufman-Roberts algorithm. We propose it to evaluate the minimal density of base stations assuring acceptable blocking probabilities for a streaming traffic of a given load per surface unit. We validate this approach by comparison of the blocking probabilities to these simulated in the similar model in which the admission control is based on the original feasibility property (instead of its sufficient condition). Our sufficient bit-rate feasibility condition can also be used to dimension the network with respect to the elastic traffic. We present a new stochastic service model with capacity sharing and interruptions, appropriate for the evaluation of the quality of real-time streaming (RTS), like e.g. mobile TV, in wireless cellular networks. The general model takes into account multi-class Markovian process of call arrivals, (to capture different radio channel conditions, requested streaming bit-rates and durations) and allows for a general resource allocation policy saying which users are temporarily denied the requested fixed streaming bit-rates (put in outage) due to resource constraints. We give expressions for several important performance characteristics of the model, including mean time spent in outage and mean number of outage incidents for a typical user of a given class. These expressions involve only stationary probabilities of the (free) traffic demand process, which is a vector of independent Poisson random variables describing the number of users of different classes. In order to analyze RTS in 3GPP Long Term Evolution (LTE) cellular networks, we specify our general model assuming orthogonal user channels with the peak bit-rates close to the theoretical Shannon's bound in the additive white Gaussian noise (AWGN) channel, which leads to the resource constraints in a multi-rate linear form. In this setting we consider a natural class of least-effort-served-first resource allocation policies, for which the characteristics of the model can be further evaluated using Fourier analysis of Poisson variables. Within this class we identify and evaluate an optimal and a fair policy, the latter being suggested by LTE implementations. We also propose some intermediate policies, which allow to solve the optimality fairness tradeoff caused by unequal user radio-channel conditions. Our results can be used for the evaluation of the quality of RTS in LTE networks and dimensioning of these networks. There have been a bulk of analytic results about the performance of cellular networks where base stations are regularly located on a hexagonal or square lattice. This regular model cannot reflect the reality, and tends to overestimate the network performance. Moreover, tractable analysis can be performed only for a fixed location user (e.g., cell center or edge user). In this paper, we use the stochastic geometry approach, where base stations can be modeled as a homogeneous Poisson point process. We also consider the user density, and derive the user outage probability that an arbitrary user is under outage owing to low signal-to-interference-plus-noise ratio or high congestion by multiple users. Using the result, we calculate the density of success transmissions in the downlink cellular network. An interesting observation is that the success transmission density increases with the base station density, but the increasing rate diminishes. This means that the number of base stations installed should be more than @math -times to increase the network capacity by a factor of @math . Our results will provide a framework for performance analysis of the wireless infrastructure with a high density of access points, which will significantly reduce the burden of network-level simulations. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. This paper deals with the performance evaluation of some congestion control schemes for elastic traffic in wireless cellular networks with power allocation control. These schemes allow us to identify the feasible configurations of instantaneous up-and downlink bit-rates of users; i.e., such that can be obtained by allocating respective powers, taking into account in an exact way the interference created in the whole, multicellular network. We consider the bit-rate configurations identified by these schemes as feasible sets for some classical, maximal fair resource allocation policies, and study their performance in the long-term evolution of the system. Specifically, we assume Markovian arrivals, departures and mobility of customers, which transmit some given data-volumes, as well as some temporal channel variability (fading), and study the mean number of users, the mean throughput i.e., the mean bit-rates, and the mean delay that these policies offer in different parts of a given cell. Explicit formulas are obtained in the case of proportional fair policies, which may or may-not take advantage of the fading, for null or infinitely rapid customer mobility. This approach applies also to a channel shared by the elastic traffic and a streaming, with predefined customer bit-rates, regulated by the respective admission policy. With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in real-time, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices’ energy efficiency by utilizing signal strength traces reported shared by other users devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudo-identical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to “good” and “bad” areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others’ signal traces, mobile devices can save energy up to 35 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions. We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that “compressive beacons,” transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which approaches the Cramer Rao Lower Bound. The algorithm exploits the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1 even for very large ( @math ) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme. In promising OFDMA systems, downlink signals originating from the same base station (BS) are orthogonal, while those from different BSs interfere with each other. As a consequence, inter-cell interference (ICI) becomes major performance degradation factor. Particularly, boundary users suffer from severe ICI in addition to the inherent near-far problem. To improve cell edge performances and support a more balanced data rate among all users, partial frequency reuse (PFR) and load-balancing schemes are investigated in this paper. We have formulated a utility maximization problem with network-wide proportional fairness (PF) as an objective in a multi-cell network with PFR. To solve this problem, we propose an offline optimal algorithm and also efficient online algorithms. Our online algorithms are based on simple inter intra-handover and cell-site selection in which a metric is changed from the signal strength to the average throughput. Through extensive simulations, we demonstrate that our online algorithms can achieve network-wide PF very closely. Compared to the conventional system with a universal frequency reuse where a user is bound to the best signal strength base station, the proposed algorithms bring two types of performance gain: interference avoidance (IA) and load balancing (LB) gain. These gains improve the system performance, especially for users at the cell boundary. During crowded events, cellular networks face voice and data traffic volumes that are often orders of magnitude higher than what they face during routine days. Despite the use of portable base stations for temporarily increasing communication capacity and free Wi-Fi access points for offloading Internet traffic from cellular base stations, crowded events still present significant challenges for cellular network operators looking to reduce dropped call events and improve Internet speeds. For effective cellular network design, management, and optimization, it is crucial to understand how cellular network performance degrades during crowded events, what causes this degradation, and how practical mitigation schemes would perform in real-life crowded events. This paper makes a first step towards this end by characterizing the operational performance of a tier-1 cellular network in the United States during two high-profile crowded events in 2012. We illustrate how the changes in population distribution, user behavior, and application workload during crowded events result in significant voice and data performance degradation, including more than two orders of magnitude increase in connection failures. Our findings suggest two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Using trace-driven simulations, we show that more aggressive release of radio resources via 1-2 seconds shorter RRC timeouts as compared to routine days helps to achieve better tradeoff between wasted radio resources, energy consumption, and delay during crowded events; and opportunistic connection sharing can reduce connection failures by 95 when employed by a small number of devices in each cell sector. The co-existence of cellular and wired networks has been exploited almost exclusively in the direction of OffLoading traffic from the former onto the latter. In this paper we claim that there exist cases that call for the exact opposite, i.e, use the cellular network to assist a fixed wired network. In particular, we show that by "OnLoading'' traffic from the wired broadband network onto the cellular network we can usefully speedup wired connections, on the downlink or the uplink. We consider the technological challenges pertaining to this idea and implement a prototype 3G OnLoading service that we call 3GOL, that can be deployed by an operator providing both the wired and cellular network services. By strategically OnLoading a fraction of the data transfers to the 3G network, one can significantly enhance the performance of particular applications. In particular we demonstrate non-trivial performance benefits of 3GOL to two widely used applications: video-on-demand and multimedia upload. We also consider the case when the operator that provides wired and cellular services is different, adding the analysis on economic constraints and volume cap on cellular data plans that need to be respected. Simulating 3GOL over a DSLAM trace we show that 3GOL can reduce video pre-buffering time by at least 20 for 50 of the users while respecting data caps and we design a simple estimator to compute the daily allowance that can be used towards 3GOL while respecting caps. Our prototype is currently being piloted in 30 households in a large European city by a large network provider. This paper explores the fundamental downlink performance limits of multicell networks when cooperation is allowed between all the base station in the network. Toward this end, we infer the asymptotic sum rate of a capacity-achieving technique (known as dirty paper coding) for the broadcast channel in the limit of a large number of transmit antennas and single-antenna users. The users have unequal SNRs characterized by a doubly-regular gain matrix and we show that this condition can be satisfied in a finite cellular network of hexagonal cells where users are placed on corners of hexagons centered on the bases. Under this placement, we can compute the asymptotic rate per user when dirty paper coding is used across base stations in a coordinated network. We extend the analysis to an infinite coordinated network and show that its performance is lower-bounded by that of a single isolated cell. Performance comparisons are also made with conventional cellular networks where coordination among bases is not allowed.