Statistical Multiplexing Gain Research Articles

Statistical Multiplexing Gain Analysis of Processing Resources in Centralized Radio Access Networks

The next generation of wireless networks faces the challenges of the explosion of mobile data traffic, the associated power consumption, and operation cost. The centralized radio access networks (Centralized RANs) architectures have been proposed to reduce the power consumption and the network operating cost. By integrating many distributed base stations' processing resources in a processing pool and sharing processing resources on demand, the overall required processing resources for the Centralized RAN can be reduced compared to the conventional RAN. This can be measured by the statistical multiplexing gain (SMG). However, most of the SMG analysis only considered the temporal traffic distribution which is not suitable for the current mobile networks. In this paper, we analyze the SMG of processing resources based on a temporal-spatial joint traffic distribution model, which considers the mobile data traffic distribution both in the time and space domains. Based on this model, we derive a formula for the SMG and also a closed-form approximation for that when the spatial traffic distribution is lognormal distribution. The theoretical analysis and simulation results show that the SMG increases with the service threshold ratio P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> , but the growth trend of SMG for different area types is not always the same. We also find that the traffic distribution parameters, such as the standard deviation of the lognormal distribution variable's natural logarithm, have a significant influence on the SMG.

Optimising the multiplexing gain in Cloud RAN using the minimum spanning tree algorithm

The cloud radio access network (Cloud-RAN) has been proposed as a cost efficient way of serving the increasing mobile traffic. The basic idea, supporting the cost efficiency, is the statistical multiplexing gain. Such a gain can be achieved when reusing the same processing power, realising BBU functions, for different, spatially distributed cells. Cells served by a single BBU pool need to be close to each other to support the mobility (handovers) and improve coordination. This article proposes the clustering algorithm, based on the minimum spanning tree (MSP). The algorithm allocates the geographically distributed sites onto a predefined set of BBU pools, keeping the clusters compact while achieving high multiplexing gain. We created, using the described algorithm, the BBU pools based on data traffic statistics from commercial network. We compared the multiplexing gain achieved with the maximum multiplexing gain achievable having given the daily profiles of analysed sites.

Statistical multiplexing and pilot optimization in fronthaul-constrained massive MIMO

Cloud-radio access network (C-RAN) has been an attractive solution in the recent years for the future generation mobile networks due to its promising benefits. However, the transport link between the remote radio unit (RRU) and the baseband unit (BBU), known as fronthaul (FH), imposes stringent requirements in terms of data rate, latency, jitter, and synchronization. In the conventional C-RAN, the FH capacity scales linearly with the number of the transmitting antennas, which has posed severe demands on the FH capacity, especially due to emerging 5G technologies such as massive MIMO. However, this can be relaxed by performing precoding at the RRUs instead of centrally at BBU, leading to FH traffic which depends on the number of currently served users. This paper adapts queueing model and spatial traffic model to exploit randomness of the user traffic to achieve statistical multiplexing gain. Through this, we showed that the required FH capacity can be reduced significantly, depending on traffic demand and its statistical properties. Furthermore, we analyzed the impacts of pilots on capacity-constrained FH.

Traffic Classification and Sifting to Improve TDM-EPON Fronthaul Upstream Efficiency

Mobile fronthaul (MF) provides connectivity between a remote radio head (RRH) and base-band processing unit (BBU) in a cloud radio access network. Dedicated MF connections between RRH and BBU are costly due to high bandwidth requirements. The cost issue becomes more pressing as cell densification increases the number of connections. A time-division multiplexing Ethernet passive optical network (TDM-EPON) is a promising solution to reduce MF cost, as it can multiplex several fronthaul flows in the MF network. However, even though mobile users transmit intermittently, RRH samples radio signals continuously, resulting in large constant line-rate connections between RRH and BBU that require huge aggregated bandwidth and render statistical multiplexing gain marginal. To improve upstream transmission efficiency by utilizing statistical multiplexing in an MF network, we propose to enhance the conventional TDM-EPON architecture. The enhanced architecture employs two techniques: (1) traffic classification (classifying upstream traffic into useful and useless by using machine-learning classifiers); and (2) useless-data sifting (sifting useless traffic to avoid the transmission of unnecessary EPON frames). We explore two feature-selection methods together with classifier biasedness in the first technique to achieve the best classification results, which then serve as input to our proposed sifting-based hybrid bandwidth allocation scheme in the second technique. Simulation results show significant improvements in terms of per-RRH traffic load, number of RRHs supported by same EPON, and signal-to-noise ratio, while keeping the end-to-end delay under 100 μs.

Optimal dynamic subcarrier allocation for dual-band OFDMA-PON supporting integrated fronthaul and backhaul in 5G networks

Abstract We propose a novel dynamic subcarrier allocation (DSCA) design for dual-band orthogonal frequency division multiple access passive optical network (DB-OFDMA-PON), supporting integrated fronthaul and backhaul services, given their stringent requirements in emerging 5G networks. An optimization problem is formulated to maximize both the bandwidth utilization and QoS guarantees. The simulation results show that our proposed DSCA achieves the desired statistical multiplexing gain and high performance in terms of throughput and delay.

On the Fronthaul Statistical Multiplexing Gain

Breaking the fronthaul capacity limitations is vital to make cloud radio access network scalable and practical. One promising way is aggregating several remote radio units (RRUs) as a cluster to share a fronthaul link, so as to enjoy the statistical multiplexing gain brought by the spatial randomness of the traffic. In this letter, a tractable model is proposed to analyze the fronthaul statistical multiplexing gain. We first derive the user blocking probability caused by the limited fronthaul capacity, including its upper and lower bounds. We then obtain the limits of fronthaul statistical multiplexing gain when the cluster size approaches infinity. Analytical results reveal that the user blocking probability decreases exponentially with the average fronthaul capacity per RRU, and the exponent is proportional to the cluster size. Numerical results further show considerable fronthaul statistical multiplexing gain even at a small to medium cluster size.

Statistical Multiplexing Gain Analysis of Heterogeneous Virtual Base Station Pools in Cloud Radio Access Networks

Cloud radio access network (C-RAN) is proposed recently to reduce network cost, enable cooperative communications, and increase system flexibility through centralized baseband processing. By pooling multiple virtual base stations (VBSs) and consolidating their stochastic computational tasks, the overall computational resource can be reduced, achieving the so-called statistical multiplexing gain. In this paper, we evaluate the statistical multiplexing gain of VBS pools using a multi-dimensional Markov model, which captures the session-level dynamics and the constraints imposed by both radio and computational resources. Based on this model, we derive a recursive formula for the blocking probability and also a closed-form approximation for it in large pools. These formulas are then used to derive the session-level statistical multiplexing gain of both real-time and delay-tolerant traffic. Numerical results show that VBS pools can achieve more than 75% of the maximum pooling gain with 50 VBSs, but further convergence to the upper bound (large-pool limit) is slow because of the quickly diminishing marginal pooling gain, which is inversely proportional to a factor between the one-half and three-fourth power of the pool size. We also find that the pooling gain is more evident under light traffic load and stringent Quality of Service (QoS) requirement.

Baseband unit cloud interconnection enabled by flexible grid optical networks with software defined elasticity

The evolution toward 5G mobile networks is characterized by supporting higher data rate, excellent end-to-end performance and ubiquitous user-coverage with lower latency, power consumption, and cost. To support this, the RANs are evolving in two important aspects. One aspect is “cloudification,” which is to pool baseband units to be centralized for statistical multiplexing gain. The other aspect is to use advanced optical technologies for digital and analog signal transmission in a cloud-based RAN. In this article, we focus on BBU cloud interconnection with optical layer technologies. Flexible grid optical networks with the enabling technologies are introduced to provide elastic, transparent, and virtualized optical paths between the BBU pools. To improve the elasticity and intelligence of C-RAN, we propose a software defined centralized control plane to coordinate heterogeneous resources from three domains: the BBU domain, radio domain, and optical domain. We report an experimental demonstration of elastic lightpath provision for cloud radio-over-flexible grid optical networks in a software-defined-networking-based testbed.

Delay-bounded resource allocation for femtocells exploiting the statistical multiplexing gain

Femtocell is an efficient solution for mobile operators to expand indoor coverage and increase network capacity. In this paper, we study the downlink resource allocation problem of two-tier macrocell---femtocell networks. We first formulate the problem as a Mixed Integer Non-Linear Program (MINLP) which aims to maximize the capacity of clustered femtocell networks subject to hard delay constraints of flows with different priorities. Next, we build $$(\rho (\theta ),\sigma (\theta ))$$(?(?),?(?)) arrival model for the traffics and apply Stochastic Network Calculus (SNC) to transforming the delay constraints into alternative minimum transmission rate requirements, then we propose a resource allocation algorithm called S-SAPCS to solve the MINLP. Simulation results show that the proposed algorithm has near-optimal performance. We also design a scheme based on deterministic network calculus to show that S-SAPCS is able to exploit the statistical multiplexing gain among multiple flows, which improves the throughput significantly.

Dynamic IQ Data Compression Using Wireless Resource Allocation for Mobile Front-Haul With TDM–PON [Invited

The time-division multiplexed-passive optical network (TDM-PON) architecture can efficiently accommodate densely deployed small cells in a centralized radio access network (C-RAN)-based future radio access (FRA). C-RAN is a type of mobile front-haul network, which connects multiple remote radio heads to a baseband unit pool by optical links. The required optical bandwidth in the mobile front-haul of FRA is very large, so it is necessary to efficiently use the optical bandwidth by using in-phase and quadrature-phase (IQ) data compression techniques. This paper proposes an IQ data compression technique that dynamically reduces the required optical bandwidth based on wireless resource allocation, and provides TDM-PON with statistical multiplexing gain. Simulation results showed the achievable compression ratio of the proposed IQ data compression technique. The feasibility of our technique was confirmed by experiments. The reduction in the average TDM-PON bandwidth was 50% when there were 60 mobile terminals, all of which required 0.18 Mbps bandwidth.

Cloud RAN for Mobile Networks—A Technology Overview

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user's needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on baseband resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This paper surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RAN.

Fundamental trade‐offs between resource separation and resource share for quality of service guarantees

There are two possible resource-allocation strategies for the quality of service (QoS) guarantee in the Internet: the resource separation and the resource share. The resource-separation strategy allows a prioritised flow to occupy its own network resource, which is physically or logically separated from resources used by other traffic. The resource-sharing strategy secures some portion of network resources for prioritised traffic, but secured resource is shared by all of prioritised flows. This study compares these two resource-allocation strategies in terms of the provided QoS level. It is found that the two strategies form a striking contrast to each other in terms of the resource usage. The resource share can benefit from the statistical multiplexing gain, but it increases the envelope of each individual multiplexed flow. In contrast to this, the resource separation does not increase the envelope of flows so much, while it does not benefit from the statistical multiplexing gain. That is, the general belief that the resource separation like the IntServ provides better QoS is not always true, and various conditions including network topology or QoS target would determine which strategy is preferable.

PON/xDSL hybrid access networks

We discuss hybrid fiber/copper access networks with a focus on XG-PON/VDSL2 hybrid access networks. We present tutorial material on the XG-PON and VDSL2 protocols as standardized by the ITU. We investigate mechanisms to reduce the functional logic at the device that bridges the fiber and copper segments of the hybrid fiber/copper access network. This device is called a drop-point device. Reduced functional logic translates into lower energy consumption and cost for the drop-point device. We define and analyze the performance of several mechanisms to move some of the VDSL2 functional logic blocks from the drop-point device into the XG-PON Optical Line Terminal. Our analysis uncovers that silence suppression mechanisms are necessary to achieve statistical multiplexing gain when carrying synchronous intermediate VDSL2 data formats across the XG-PON.

Overbooking in Mobile Backhaul

The rapid increase of mobile data subscribers and deployment of broadband radio access networks push mobile operators to upgrade their mobile backhaul from expensive legacy TDM transport to carrier Ethernet for cheaper operational expenditures. The Metro Ethernet Forum has defined a set of Ethernet Virtual Connection services that are adopted to provide scalable Ethernet transport for mobile backhaul. However, these services usually address single cell site backhaul per UNI handoff, neither considering statistical multiplexing gain at a hub site which aggregates backhaul traffics of multiple cell sites, nor supporting Quality-of-Service provisioning in overbooked backhaul pipe. This paper proposed an efficient carrier Ethernet overbooking solution for mobile backhaul. A statistical estimation method has been developed for deriving a safe overbooking factor at a given UNI. Then two efficient transport architectures were proposed to support bandwidth sharing in cellular cluster with overbooked backhaul bandwidth in carrier Ethernet. A novel bandwidth control algorithm has been derived at customer edge device to provide Quality-of-Service for multimedia traffics over overbooked UNI, with SLA policing and protection on high priority services. Experimental data analysis and simulations have showed that our new schemes can benefit mobile operators in resource utilization efficiency, carrier Ethernet cost saving and backhaul performance.

Perspectives on network calculus

ACM Sigcomm 2006 published a paper [26] which was perceived to unify the deterministic and stochastic branches of the network calculus (abbreviated throughout as DNC and SNC) [39]. Unfortunately, this seemingly fundamental unification---which has raised the hope of a straightforward transfer of all results from DNC to SNC---is invalid. To substantiate this claim, we demonstrate that for the class of stationary and ergodic processes, which is prevalent in traffic modelling, the probabilistic arrival model from [26] is quasi-deterministic, i.e., the underlying probabilities are either zero or one. Thus, the probabilistic framework from [26] is unable to account for statistical multiplexing gain, which is in fact the raison d'être of packet-switched networks. Other previous formulations of SNC can capture statistical multiplexing gain, yet require additional assumptions [12], [22] or are more involved [14], [9] [28], and do not allow for a straightforward transfer of results from DNC. So, in essence, there is no free lunch in this endeavor. Our intention in this paper is to go beyond presenting a negative result by providing a comprehensive perspective on network calculus. To that end, we attempt to illustrate the fundamental concepts and features of network calculus in a systematic way, and also to rigorously clarify some key facts as well as misconceptions. We touch in particular on the relationship between linear systems, classical queueing theory, and network calculus, and on the lingering issue of tightness of network calculus bounds. We give a rigorous result illustrating that the statistical multiplexing gain scales as Ω(√ N ), as long as some small violations of system performance constraints are tolerable. This demonstrates that the network calculus can capture actual system behavior tightly when applied carefully. Thus, we positively conclude that it still holds promise as a valuable systematic methodology for the performance analysis of computer and communication systems, though the unification of DNC and SNC remains an open, yet quite elusive task.

Path-Based QoS Provisioning for Optical Burst Switching Networks

Quality-of-service (QoS) provisioning is an essential feature in next-generation networks. In this paper, we investigate the ability of optical burst switching (OBS) networks to guarantee loss-free transmission inside the network for guaranteed bursts. More specifically, we propose a QoS approach, called path-based QoS provisioning (PQP), to provide absolute QoS provisioning for OBS networks. PQP relies on: 1) routing and wavelength assignment to establish, whenever possible, nonoverlapping paths between each pair of OBS edge nodes and 2) a synchronization scheme to guarantee QoS when the solution in 1) contains overlapping paths because of the limited number of wavelengths. For 1), we propose a routing and wavelength assignment approach, which uses an efficient integer linear programming (ILP) model to determine routing paths and a tabu search algorithm to assign wavelengths to these paths. For 2), we propose a path synchronization scheme, called path-based synchronous transmission scheme (PST). PST synchronizes the transmissions in each set of overlapping paths while maximizing the capacity of each path to transmit guaranteed traffic and guaranteeing fairness when allocating bandwidth to conflicting paths; this is performed using efficient ILP formulations. To improve the performance of best effort traffic and preserve statistical multiplexing gain and high resource utilization of the OBS network, we propose a wavelength selection scheme, called path-based best effort wavelength selection scheme, to send best effort bursts. Simulation results using ns-2 simulator show that PQP successfully provides absolute QoS provisioning for guaranteed traffic and improves significantly the performance of best effort traffic.

On the optimality of packet-oriented scheduling in photonic switches with delay lines

Addressing the bandwidth inefficiency problem of current IP over DWDM backbone switching, Optical Packet/Burst Switching (OPS/OBS) provide viable solutions, capitalizing on statistical multiplexing gain, through packet-oriented scheduling. To resolve packet/burst contention, the involved photonic switches contain wavelength converters and fiber delay lines, controlled through a channel and delay selection (CDS) algorithm. Recently proposed CDS algorithms all rely on heuristics, of which the optimality is unexamined to date.<br>&nbsp;&nbsp This paper presents an in-depth analysis of the optimality of CDS algorithms. Methodologically, we rely on Markov chain analysis for performance evaluation, combined with a discrete Markov Decision Process formulation of the optimization problem, optimized for fast calculation, allowing to determine the exact optimum of a specific given setting of the switch, through numerical algebra solution techniques. Results point out that, for the basic switch setting assumed, of all known CDS algorithms, an algorithm called MING (MINimal Gap) is close to optimal, but never strictly optimal. Various graphs support this, showing that an algorithm optimal for any traffic load cannot (in general) be devised. Results for several other switch settings further confirm this, showing how known CDS algorithms might be modified, to attain improved control robustness.

On Network Planning for Video Services Over IP Networks

Video transmission over IP requires very high data rate for a good performance. Variable bit rate (VBR)-coded video is more popular than constant bit rate (CBR)-coded video due to its statistical multiplexing gain and stable quality level. This phenomenon forms a real challenge for network planning and especially for appropriate capacity evaluation. In this work, we propose a new capacity planning model that is especially designed for VBR video transmission over IP networks. This new method takes the quality level and the desired delay threshold as its main design parameters. We investigate this new method by means of simulations and we finally validate its applicability for real-time video transmission with respect to different system parameters.

Survey of deterministic and stochastic service curve models in the network calculus

In recent years service curves have proven a powerful and versatile model for performance analysis of network elements, such as schedulers, links, and traffic shapers, up to entire computer networks, like the Internet. The elegance of the concept of service curve is due to intuitive convolution formulas that determine the data departures of a system from its arrivals and its service curve. This fundamental relation constitutes the basis of the network calculus and relates it to systems theory, however, under a different, so-called min-plus algebra. As in systems theory, the particular strength of the minplus convolution is the ability to concatenate tandem systems along a network path. This facilitates the notion of network service curve that has the expressiveness to characterize whole networks by a single transfer function. This paper surveys the state-of-the-art of the deterministic and the recent probabilistic network calculus. It discusses the concept of service curves, its use in the network calculus, and the relation to systems theory under the min-plus algebra. Service curve models of common schedulers and different types of networks are reviewed and methods for identification of a system's service curve representation from measurements are discussed. After recapitulating the state of knowledge on time-varying min-plus systems theory, stochastic service curve models are surveyed. These models allow utilizing the statistical multiplexing gain in a network calculus framework that features end-to-end network analysis by convolution of service curves.

Group-Mode Hopping for Collision Mitigation in Orthogonal Code-Hopping Multiplexing

Orthogonal code-hopping multiplexing (OCHM) is a statistical multiplexing scheme designed to increase the number of allowable downlink channels in code-division multiple-access (CDMA) systems. OCHM makes it possible to use orthogonal code resources more efficiently than the conventional dedicated codeword allocation/multiplexing schemes in CDMA2000 and wideband CDMA (WCDMA) systems. Collision-allowable hopping in the OCHM system yields a statistical multiplexing gain. However, collisions may degrade the bit error rate (BER) performance. A group-mode hopping scheme is proposed as a collision mitigation scheme for OCHM. The performance of the proposed scheme is evaluated using numerical analysis and simulations.