Providing performance guarantees is one of the critical objectives of recent and future communication networks, toward which regulations, i.e., constraints on key system parameters, have played an indispensable role. This is the case for large wireless communication networks, where spatial regulations (e.g., constraints on intercell distance) have recently been shown, through a spatial network calculus, to be essential for establishing provable wireless link-level guarantees. In this work, we focus on performance guarantees for the downlink of cellular networks where we impose a hardcore (spatial) regulation on base station (BS) locations and evaluate how BS scheduling (which controls which BSs can transmit at a given time) impacts performance. Hardcore regulation is the simplest form of spatial regulation that enforces a minimal distance between any pair of transmitters in the network. Within this framework of spatial network calculus, we first provide an upper bound on the power of total interference for a spatially regulated cellular network, and then, identify the regimes where scheduling BSs yields better link-level rate guarantees compared to scenarios where base stations are always active. The hexagonal cellular network is analyzed as a special case. The results offer insights into what spatial regulations are needed, when to choose scheduling, and how to potentially reduce the network power consumption to provide a certain target performance guarantee.

This paper studies the radio resource management (RRM) for the uplink (UL) of a cellular system with codebookbased hybrid beamforming. We consider the often neglected but highly practical multi-channel case with fewer radio frequency chains in the base station than user equipment (UEs) in the cell, assuming one RF chain per UE. As for any UL RRM, a per-time slot solution is needed as the allocation of power to subchannels by a UE can only be done once it knows which subchannels it has been allocated. The RRM in this system comprises beam selection, user selection and power allocation, three steps that are intricately coupled and we will show that the order in which they are performed does impact performance and so does the amount of coupling that we take into account. Specifically, we propose 4 online sequential solutions with different orders in which the steps are called and of different complexities, i.e., different levels of coupling between the steps. Our extensive numerical campaign for a mmWave system shows how a well-designed heuristic that takes some level of couplings between the steps can make the performance exceedingly better than a benchmark.

Radio resource management of the downlink of multi-user multiple-input multiple-output systems with multi-antenna users is considered to evaluate the performance of three zero-forcing precoding strategies: 1) Block Diagonalization (BD) where all streams are used for each selected user; 2) Coordinated-Transmit-Receive-1$(\mathbf{CTR}_{\mathbf{1}})$where only the strongest stream is used for each scheduled user; 3) Coordinated-Transmit-Receive-Flexible$(\mathbf{CTR}_{\mathbf{F}})$that allows a flexible stream allocation per selected user. Although the more complex$\mathbf{CTR}_{\mathbf{F}}$has the potential for better performance due to its flexibility, it might be difficult to implement it in practice. Hence, it is crucial to comprehend when the simpler CTR1or BD can be used instead of CT$\mathbf{R}_{\mathbf{F}}$. Our analysis compares the precoding strategies within$\mathbf{3GPP}$-compliant scenarios using realistic modulation and coding schemes in systems featuring large numbers of users and Base-Station (BS) antennas. We compare the performance of these precoding strategies under Sum-Rate (SR) maximization and Proportional Fairness (PF) and show that previous research conclusions carried out for SR maximization only hold for a small number of BS antennas. Indeed, for SR maximization, BD matches the performance of$\mathbf{CTR}_{\mathbf{F}}$when BS antenna arrays are sufficiently large. For PF, the results are more nuanced, depending significantly on system parameters.

As 5G networks proliferate globally, the need for accurate, reliable, and scalable positioning solutions has become increasingly critical across industries, such as Internet of Things (IoT), healthcare, and autonomous systems. This article comprehensively reviews current and emerging positioning techniques within 5G, exploring the advancements enabled by sidelink communication, reconfigurable intelligent surfaces (RISs), machine learning, and massive multiple-input–multiple-output. We examine the evolution of 5G positioning as defined by key 3GPP releases, and provide a comparative analysis of the techniques in terms of accuracy, cost, and robustness. The review also highlights key challenges, including non-line-of-sight (NLOS) environments, real-time data processing, and security concerns, which must be addressed for widespread adoption. Finally, we discuss future directions for 5G-Advanced and 6G positioning technologies, offering insights into potential improvements and the ongoing evolution of the field.

We study the uplink resource management of a multi-user multiple-input-multiple-output single cell for Zero-Forcing receive combining transmission. We consider jointly power allocation, user selection and modulation and coding scheme selection over multiple subchannels. Our contributions are twofold: we first propose a quasi-optimal offline algorithm that provides a target performance and then design and validate an efficient online proportional fair algorithm that performs the above steps. Due to user power constraints, the offline optimization is conducted jointly for all subchannels within a time slot, a computationally intensive task, prompting the proposal of a greedy offline algorithm that we validate in two ways: 1) for a small number of users, by solving the general problem to quasi-optimality and 2) for a larger number of users, by solving again to quasi-optimality a transformed version of the general problem when the channels are assumed flat. From the offline study, we find that, given the right user selection, equal power allocation can be employed without much degradation in performance. We also see that the number of channels allocated to users varies widely depending upon their channel gains. Using these insights, we propose our efficient real-time online algorithm that has runtime competitiveness with a state-of-the-art benchmark.

Open-source 5G cellular networks are becoming important to reduce cost and enable more vendors to enter the booming 5G market. Many open-source software are available for both the Radio Access Network (RAN) and the core network and verifying their interoperability and their performance when combined is critical. This has not been studied as yet and it is the topic of this paper. We have deployed a 5G-standalone platform to assess the interoperability of various core and RAN open-source software developed by different developers and to study how different testbeds comprising different core and RAN open-source software perform. For our experiments, we have selected srsRAN _Project, and OpenAirInterface SG RAN, as the leading open-source RAN soft-ware. Similarly, we have selected OpenSGS, and OpenAirInterface SG Core to be the open-source 5G core software. Our performance results show that in the RAN domain OpenAirInterface SG RAN wins on downlink rate, and latency while srsRAN _Project wins on uplink rate. Moreover, we did not see a significant difference in the performance of the two 5G core software.

While multi-user (MU) massive MIMO is a critical technology for next generation wireless systems, its complexity poses significant operational challenges as it entails several processes. These include user selection, precoding, power distribution among the users, and Modulation and Coding Scheme (MCS) selection. While many studies have been conducted on MU-MIMO, most have made invalid assumptions (e.g., every non-zero signal received at a user yields a non-zero rate) or excluded some essential steps (e.g., MCS selection). We revisit the problem of operating a single-cell massive MIMO network with zero-forcing precoding, and develop real-time network operation algorithms. First, we relax the real-time constraint and perform an offline study to obtain a target performance for online algorithms. The joint problem can be solved exactly offline for small to medium sized settings using branch-reduce-and-bound. For larger settings, we note that, given a choice of user selection, the problem reduces to a power distribution problem that can be solved exactly. Thus, the joint problem reduces to a search over user-sets where for each considered user-set, a power distribution problem is solved. We propose various search methods and evaluate their performance. For online operation, we leverage the problem structure to propose an algorithm based on three ideas: i) grouping, 2) MCS-aware power distribution, and 3) an iterative process to remove users that see a zero rate. The algorithm achieves 94% of the performance target set by the offline study results.

This paper investigates downlink radio resource management (RRM) in millimeter-wave systems with codebook-based hybrid beamforming in a single cell. We consider a practical but often overlooked multi-channel scenario where the base station is equipped with fewer radio frequency chains than there are user equipment (UEs) in the cell. In this case, analog beam selection is important because not all beams preferred by UEs can be selected simultaneously, and since the beam selection cannot vary across subchannels in a time slot, this creates a coupling between subchannels within a time slot. None of the solutions proposed in the literature deal with this important constraint. The paper begins with an offline study that analyzes the impact of different RRM procedures and system parameters on performance. An offline joint RRM optimization problem is formulated and solved that includes beam set selection, UE set selection, power distribution, modulation and coding scheme selection, and digital beamforming as a part of hybrid beamforming. The evaluation results of the offline study provide valuable insights that show the importance of not neglecting the constraint and guide the design of low-complexity and high-performance online downlink RRM schemes in the second part of the paper. The proposed online RRM algorithms perform close to the performance targets obtained from the offline study while offering acceptable runtime.

5G, the fifth generation of mobile networks, promises new services, faster speeds, lower latency, and increased network capacity. A 5G network has three main elements: the Radio Access Network (RAN) which can be further divided into a hardware component, called software-defined radio (SDR), a software component, the core network and the User Equipment (UE). Recent years have seen the emergence of an “open” paradigm where the different elements of a 5G network are designed by different developers, and as a result can be separately modified and then integrated to enhance network functionality. This paper presents a framework to compare the impact of different elements on the performance of an end-to-end 5G standalone testbed. In particular, using open5GS as the core and 5G modems as the UE(s), we compare the performance of the recently released “O-RAN native suite, srsRAN-Project” to its srsRAN predecessor for two different SDRs (Ettus USRPs B210 and X410), over wireless and wired channels, in a single cell with one or two UEs. It is concluded that srsRAN-Project, with X410 as the SDR, provides the most stable and consistent performance over wired and wireless channels in both single-UE as well as multi-UE testbeds.

We study the planning of a rural 5G multi-user massive MIMO fixed wireless access system to offer fixed broadband service to homes. Specifically, we aim to determine the user limit, i.e., the maximum number of homes that can simultaneously receive target minimum bit rates (MBRs) on the downlink (DL) and on the uplink (UL) given a set of network resources and a cell radius. To compute that limit, we must understand how resources should be shared between the DL and UL and how user and stream selection, precoding and combining, and power distribution should be performed. We use block diagonalization and propose a static grouping strategy that organizes homes into fixed groups (of possibly different sizes) in the DL and UL; then we develop a simple approach to compute the user limit that we validate numerically. We study the impact of group size and show that smaller groups yield larger user limits in a 3.5 GHz band. We show how the user limit at different cell radii is impacted by the system bandwidth, the number of antennas at the base station and homes, the transmit power, and the MBRs. Lastly, we offer insights into how the network could be operated.

While mmWave bands provide a large bandwidth for mobile broadband services, they suffer from severe path loss and shadowing. Multiple-antenna techniques such as beamforming (BF) can be applied to compensate the signal attenuation. We consider a special case of hybrid BF called per-stream hybrid BF (PSHBF) which is easier to implement than the general hybrid BF because it circumvents the need for joint analog-digital beamformer optimization. Employing BF at the base station enables the transmission of multiple data streams to several users in the same resource block. In this paper, we provide an offline study of proportional fair multi-user scheduling in a mmWave system with PSHBF to understand the impact of various system parameters on the performance. We formulate multi-user scheduling as an optimization problem. To tackle the non-convexity, we provide a feasible solution and show through numerical examples that the performance of the provided solution is very close to an upperbound. Using this framework, we provide extensive numerical investigations revealing several engineering insights.

We investigate the performance of a hybrid non-orthogonal multiple access (NOMA) multi-cell downlink system (called hybrid as different users can have different successive interference cancellation (SIC) capabilities) by first formulating and solving a centralized proportional fair scheduling genie-assisted problem that jointly performs user selection, power allocation, power distribution, and modulation and coding scheme (MCS) selection. While such a genie is practically infeasible, it upper bounds the achievable performance. The results indicate that hybrid NOMA with a maximum of 2 multiplexed users can bring significant gains over a traditional OMA system (as long as enough users have the maximum SIC capability). Additionally, results show that the simple equal power allocation scheme (often used in the literature) yields performance lower than half the upper bound. Thus, we propose a simple static coordinated power allocation scheme across all cells for NOMA using a simple power map that is easily calibrated offline and show that with the calibrated power map, performance improves by 80%. Finally, we focus on the online scenario and propose a family of practical scheduling algorithms, each of them exhibiting a different trade-off between complexity (i.e., run-time) and performance.

We study the problem of optimally and simultaneously sizing solar photovoltaic (PV) and storage capacity in order to partly or completely offset grid usage. While prior work offers some insights, researchers typically consider only a single sizing approach. In contrast, we use a firm theoretical foundation to compare and contrast sizing approaches based on robust simulation, robust optimization, and stochastic network calculus. We evaluate the robustness and computational complexity of these approaches in a realistic setting to provide practical, robust advice on system sizing.

Global connectivity is at an all-time high, and more users than ever before are participating in the online ecosystem. Despite this exciting phenomenon, opportunities to access the online world are not shared equally among the global population. Socioeconomic, political, and geographic factors all play roles in determining the extent to which one can be an online participant [1]. This resultant digital divide creates a social disparity between those who have reliable access to online content and can take advantage of all that the Internet has to offer and those who do not and miss out on those opportunities [2].

We study joint proportional-fair (PF) resource allocation (RA), including user selection, linear precoding design, power optimization, and modulation and coding scheme selection, in a single-cell downlink massive MIMO (m-MIMO) system over consecutive time-slots when taking per-antenna power constraints (PAPCs) into account. We formulate the general PF joint RA optimization problem as a weighted sum-rate maximization problem at each time-slot and develop a solution technique to obtain a quasi-optimal feasible solution via the introduction of auxiliary variables and a carefully chosen approximation of the spectral-efficiency function. To obtain results for larger settings (i.e., larger number of antennas and users), we propose an approximation to the general problem that yields quasi-optimal feasible solutions. Moreover, we consider state-of-the-art linear precoding techniques and propose a general heuristic RA scheme that takes PAPCs into account. Numerical results show that PAPCs have significant impact on performance even for a very large number of antennas, and that the best existing linear precoding technique, RZFT (regularized zero-forcing transmission) performs very well when RA is performed carefully as long as the PAPCs are not tight. However, RZFT is far from optimal under tight PAPCs, which highlights the need for practical PAPC-aware precoding techniques in this regime.

This paper proposes a comprehensive study of uplink scheduling in multi-cell OFDMA networks. We first focus on two scenarios for the homogeneous case, one without and one with a Cloud-RAN (C-RAN), and explore how to design efficient practical uplink schedulers for those scenarios. To compute the best achievable performance (BAP) under complete information, we study a centralized multi-cell scheduler. To this end, we formulate an MINLP problem and show how to solve it quasi-optimally. Then, we study the performance of an existing practical local benchmark scheduler (LBM) in terms of goodput and losses. We compare its performance to BAP and show that LBM only yields 44 percent of BAP. To reduce this performance gap, we propose two practical enhancements for LBM, one per scenario. The enhanced scheduler for the first scenario yields 51 percent of BAP (70 percent for the second). To reduce the gap further, we propose a new scheduler inspired by soft-frequency reuse (SFR). Its performance is 69 percent (resp. 83 percent) of BAP. It outperforms LBM by 56 percent for the scenario without C-RAN (84 percent with C-RAN). We finally extend our SFR-based scheduler to heterogeneous networks and show that it outperforms LBM by 53 percent for the scenario without C-RAN (96 percent with C-RAN).

In this work, we propose a robust planning tool that allocates power statically in homogeneous and heterogeneous cellular networks with non-regular base station (BTS) placement, to mitigate interference and improve overall performance. Each BTS will use the total available spectrum, but it will divide it into multiple sub-bands, and each BTS will transmit with a specific pre-computed power on each sub-band. We refer to such a power allocation as a power map. Our offline planning tool computes a robust power map for a given topology, by solving a non-convex, non-linear optimization problem, through simple transformations, based on geometric programming. The power map is computed based solely on the network topology, and it is made available to all BTSs that use it throughout the network operation to perform scheduling using a fast quasi-optimal online algorithm that we propose. We evaluate our planning tool for different homogeneous and heterogeneous networks (HetNets), first in a static setting where scheduling is performed optimally and then in a dynamic setting when scheduling is performed with our online scheduler. Results show that our solution significantly outperforms a classical equal power/fixed frequency reuse scheme in terms of sum-rate, by up to 30% in homogeneous networks and by up to 70% in HetNets.

An increased density of access points is common today in WiFi deployments, and more and more parameters need to be configured in such networks. In this paper, we question current industrial guidelines for both residential and enterprise scenarios. More precisely, we investigate the joint channel, power, and carrier sense threshold allocation problem in IEEE 802.11ac networks, showing that the current practice, which is to use narrower channels at maximum power when the deployment is dense, yields much worse performance than a solution using the widest possible channel with a much lower power.

In cellular networks, a local uplink scheduler cannot compute good estimates of the inter-cell interference even with exact channel state information (CSI). Losses (due to decoding errors) will hence frequently occur. The maximum achievable performance (MAP) can only be achieved when all the cells are scheduled simultaneously centrally so as to manage interference and power optimally. In this paper, we focus on the performance of a practical system with local schedulers (i.e., each cell is scheduled independently) and compare it to MAP. We also study how to improve the performance of practical systems when a Cloud Radio Access Network (C-RAN) is present, with simple coordination schemes. To this end, to compute MAP, we first formulate an offline system-wide scheduling problem and transform it into a more tractable signomial problem that we solve quasi-optimally using an iterative algorithm. Since this problem requires all the channel information in the system and it has a high computational complexity, it is not suitable for a real-time system. Then, we show that a practical system using an efficient local scheduler (in each cell) yields a much lower performance than MAP. To decrease this performance gap, in a system with a C-RAN, we propose a very simple and fast scheme to coordinate the scheduling in all cells and show that it improves the performance significantly (in terms of throughput and losses) even when only partial CSI is available.

Providing a consistent data rate to mobile users will be a very important feature of next generation systems, i.e., 5G, especially for services such as live video-streaming, online gaming, etc. This could lead to an increased user satisfaction with these services. In this paper, we perform the analysis to determine the maximum consistent data rate that can be offered to a (high paying) class of mobile users, both within a cell and within a region covered with multiple cells, given certain available resources. We do this for two cases: 1) when the number of active users in the class is constant, and 2) for a varying number of users being simultaneously present and active in the class. The analysis is performed under some independence assumptions, but we validate our results with extensive realistic simulations where the assumptions are relaxed. We show that providing consistent rate is rather expensive because a large percentage of the available resources remain unused most of the time. However, the unused resources can be shared (possibly equally) by the users in the group. In that case the consistent rate can be seen as a guaranteed minimum rate. The other option is to allocate the unused resources to a class of best effort users. We show that using any of these options will result in significant performance improvements.

We propose smart roaming (SR), a cooperation technique between cellular telcos operating in the same region, that enables users to roam for performance reasons (even if they are covered by their operator). Within a region, base stations of different operators are sometimes co-located but, in that case, the sectors are rarely aligned. SR leverages spatial diversity to enhance spectrum usage efficiency. Simply put, an edge user of an operator might be a “good user” for another one. This paper answers the following research questions: 1) Can significant gain be obtained with SR? 2) What are the factors that affect the gain? 3) How to manage operator heterogeneity to avoid that a large operator cross subsidizes a smaller one? and 4) How to implement and manage SR in an online fashion while keeping the signaling information manageable? We answer the first three questions by proposing a snapshot model for the downlink that shows that SR can indeed provide significant gain without yielding cross-subsidies if done properly. We then propose two schemes to implement and manage SR online and evaluate them via extensive simulations.

An important evolution of the users’ needs is represented by the on-demand access to the network, storage, and compute resources in order to dynamically match the level of resource consumption with their service requirements. The response of the network providers is to transition to an architecture based on softwarization and cloudification of the network functions. This is the rationale for the deployment of network functions virtualization (NFV) where virtual network functions (VNFs) may be chained together to create network services. Efficient online routing of demand across nodes handling the functions involved in a given service chain is the novel problem that we address in this paper. We provide an original formulation of this problem that includes link and CPU capacity constraints and is based on the construction of an expanded network. We derive the exact mathematical formulation and propose several heuristic algorithms taking into account the main system’s parameters. We conclude by deriving some interesting insights both about the algorithms and the network performance by comparing the heuristics with the exact solutions.

Uplink scheduling in cellular networks is challenging due to power and interference management. Typically, each cell performs local scheduling, which requires estimation of inter-cell interference (ICI) to compute the appropriate modulation and coding scheme (MCS) based on the Signal-to-Interference-plus-Noise-Ratio (SINR) for each allocated resource block. Since schedules of neighboring cells are unknown to schedulers, the SINR can be badly estimated, which causes resource losses or under-utilization. The benchmark uplink scheduler we study in this paper produces a high goodput at the cost of significant resource losses, because it does not take the possibility of losses into account. Resource losses imply retransmissions, hence, high variability in delay. Therefore, a scheduler should be evaluated in terms of its goodput/loss trade-off. We propose a novel uplink scheduler that is inspired by Soft Frequency Reuse (SFR) and uses an MCS selection that takes the probability of losses into account, i.e., it selects an MCS that maximizes the effective rates seen by users, while keeping the loss probability below a threshold e. We show that the proposed scheduler yields significantly better goodput/loss trade-off than the benchmark scheduler.

The return link of broadband satellite systems has recently received more attention due to the spread of multi-beam antennas which enable spatial frequency reuse, and thus increase drastically the number of users that can potentially be served by one satellite. While interference isolation has so far been the way to go, with regular four-color frequency reuse scheme, there is a growing interest in densifying the frequency usage as is being done in cellular networks. In this paper, we address the return link radio resource allocation challenges, from spectral resource allocation to user scheduling, including modulation and coding scheme (MODCOD) selection. Our contributions highlight the potential gains of a two-color scheme and shed light on several levers to reap its benefits through interference management. We first consider the possibility to use a two-color scheme, while keeping the MODCOD selection and the scheduling local to each beam and we show that even though it yields a potential performance gain (+16%) with respect to the state of the art (SoA) (based on four colors), it is not viable due to a very high-block loss rate. Therefore, we propose a simple-yet fast and efficient-coordinated MODCOD selection process that alleviates the need of estimating interference and reduces drastically decoding failures. This coordination step offers significant gains (+58%) over the SoA, while leaving the per beam scheduler unchanged. Finally, we formulate a joint user scheduling and MODCOD selection problem across all beams and propose an offline heuristic to solve it efficiently. We obtain a 83% gain with respect to the SoA, but with higher computational complexity. Still, it confirms the great potential of coordinated scheduling.

We consider a scenario in which an operator installs (small cell) base stations on top of city buses to offer better quality of experience (QoE) to their passengers. In that case, providing a consistent backhaul rate (i.e., a constant rate at all times) to these base stations could help mitigate the effects of mobility on the QoE. Specifically, we perform the analysis to determine the maximum consistent backhaul rate that can be offered to a bus on a given route, given the resources allocated by the operator to backhauling, by taking advantage of the fact that different buses on that route will see different conditions at a given time. We also consider the case where we allow a small outage probability, i.e., that the consistent rate is not provided for a small proportion of time. We show that by allowing an outage probability of only 1% we can increase the achievable backhaul rate by 50%. We then show how to compute the Pareto frontier (rate region) of the achievable consistent backhaul rates when there are two bus routes. The analysis is performed under an independence assumption and hence we validate our results by simulations. Altogether, the cost of consistency is very high, but it can be partly mitigated by allocating the unused backhaul capacity to best effort services in real time.

We introduce a new, simple, yet effective scheme for reducing the impact of receiver buffer blocking in Multipath TCP (MPTCP). This blocking primarily occurs when the paths have different characteristics. This phenomenon is due to the limited size of the MPTCP receiver buffer. In a nutshell, our scheme allows, under certain conditions, the retransmission of a segment by a different interface than the one used originally and the closure of the badly behaving TCP connection. Our scheme anticipates the problem by opening multiple TCP connections for each interface, but only uses one at a given time. It detects when a segment needs to be retransmitted from another interface, closes the ongoing connection on the original interface, and starts using one of the backup connections. We show through NS3 simulations that our scheme improves MPTCP goodput, with a gain of 19% compared to other MPTCP schemes, and provides a smooth data delivery to the application layer. This last feature is of utmost importance for streaming applications. Furthermore, while other MPTCP schemes fail to perform better than the best single path TCP for some scenarios, our proposed scheme always outperforms the best single path TCP.

Many new mobile applications create traffic among cellular users. We define intra-cellular traffic as the traffic from one cellular user to another user in the same cellular network. This type of traffic introduces new challenges for cellular network operators. Most work in the literature focuses on the possibility to utilize the direct links between those users, if they are close to each other (this is called device-to-device (D2D) communication), to by-pass the base station. However, implementing D2D is not easy, especially because detecting that a traffic is intracellular is difficult. In this paper, we assume that we know how to detect if a traffic is intra-cellular or not and focus on designing a type-aware scheduler (i.e., a scheduler which has the information on the type of traffic) in a case where direct communications between users is not enabled. This scheduler can be seen as the benchmark against the case where direct communications are allowed. We show that performance gain can be obtained by jointly scheduling the uplink and downlink with respect to the case where the scheduler is blind to the types. We show for a homogeneous network that when the traffic types are known to a scheduler, a significant performance gain (up to 28%) can be achieved compared to the case where the traffic types are not known. We also analyze heterogeneous networks that consist of macro cells and small cells and show that up to a 36% performance gain can be obtained by performing type-aware user association jointly with user scheduling.

We consider a heterogeneous cellular network (HetNet) on the downlink and focus on multi-cell selection (MCS). MCS allows each user to associate with and receive data from multiple base stations (BTSs) at once, potentially boosting the network performance. We formulate a fully coordinated realistic MCS problem that provides an upper-bound on the network performance which is 20% above that of the state-of-the-art single-cell selection (SCS). However, this improvement comes at the cost of inter-BTS coordination which is not so easy to perform in practice. Hence, we focus on how to obtain this performance gain in two practical scenarios; 1) a conventional HetNet with no inter-BTS coordination, and 2) a Centralized Radio Access Network (C-RAN) HetNet. In Scenario 1, we show that SCS with periodic individual opportunities for re-associations along with local Round Robin scheduling (which requires no inter-BTS coordination) can outperform the state-of-the-art SCS by 17% without incurring a huge cost in signaling. In Scenario 2, we show that we can almost reach the upper-bound without paying for the complexity in coordinated scheduling using a simple two-step heuristic.

Radio resource allocation (RA) in heterogeneous networks (HetNets) is an important problem. In this paper, we focus on two RA schemes proposed in the literature for future HetNets: almost blank subframes (ABS) and partially shared deployment (PSD). Through extensive analysis and simulations, we show that PSD outperforms ABS on many fronts across different α-fairness criteria and traffic scenarios.

The wide range of performance characteristics of storage technologies motivates the use of a hybrid energy storage system (HESS) that combines the best features of multiple technologies. However, HESS design is complex, in that it involves the choice of storage technologies, the sizing of each storage element, and deciding when to charge and discharge each underlying storage element (operating strategy). We formulate the problem of jointly optimizing the sizing and the operating strategy of an HESS that can be used for a large class of applications and storage technologies. Instead of a single set of storage element sizes, our approach determines the Pareto-optimal frontier of the sizes of the storage elements along with the corresponding optimal operating strategy. Thus, as long as the performance objective of a storage application (such as an off-grid microgrid) can be expressed as a linear combination of the underlying storage sizes, the optimal vector of storage sizes falls somewhere on this frontier. We present two case studies to illustrate our approach, demonstrating that a single storage technology is sometimes inadequate to meet application requirements, unlike an HESS designed using our approach. We also find simple, near-optimal, and practical operating strategies for these case studies, which allows us to gain several new engineering insights.

Base station operation consumes a lot of energy, a considerable amount of which can be saved by switching off base stations during low user demand (for example, at night). Base station switching (BSS) can result in loss in coverage if not performed properly. We show that coverage is closely related to scheduling via power management and that the bottleneck is typically the uplink. To save energy, we propose a set of BSS patterns, at a global system-level, that have the potential to provide full coverage if the appropriate schedulers are used. We further show that the existing benchmark uplink scheduling schemes do not provide full coverage when BSS is used in urban as well as rural macro-cell environments (the downlink benchmark scheduling scheme provides full coverage only for some of the BSS patterns). Hence, we propose novel scheduling schemes for both uplink and downlink that realistically model interference, ensure full coverage, and provide good energy-performance trade-offs for the proposed BSS patterns. We also present a low complexity high performance heuristic for the proposed uplink scheduler. Finally, we show the presented models and results can be used to quantify, offline, the energy-performance trade-offs under different operating scenarios.

We study the impact of the limited capacity of backhaul links on downlink user scheduling in a heterogeneous network comprising macro base stations and small cells. Assuming a tree topology of the backhaul network, we formulate a backhaulaware global α-fair time-domain user scheduling problem and study it under three different scenarios of backhaul limitations. For the scenario where the backhaul links are not the bottleneck, we derive closed-form scheduling solutions to the scheduling problem under certain assumptions. For the scenario where the backhaul links between the macro base station and the small cells are the bottleneck, we show that the global α-fair user scheduling problem can be decomposed into a set of independent local α-fair user scheduling problems. However, unlike the previous case, a local scheduler in this case is not of a unique type but can be of one of three types, depending on the available backhaul capacity. We completely characterize these three types and also propose a simple heuristic for optimal α-fair scheduling. When the link between the macro base station and the core network is a potential bottleneck, we show how each base station can still perform a local scheduling as in the previous case as long as there is a master problem that allocates feasible virtual backhaul capacities to each BS. However, computing the optimal virtual capacities is complex and expensive in terms of the amount and frequency of information exchanges. For this scenario, we propose realization-agnostic heuristic schemes that are simple to implement and perform quite well.

Providing high data rates with minimum energy consumption is a crucial challenge for next generation wireless networks. There are few papers in the literature which combine these two issues. This paper focuses on multi-hop wireless mesh networks using a MAC layer based on Spatial Time Division Multiple Access (S-TDMA). We develop an optimization framework based on linear programming to study the relationship between throughput and energy consumption. Our contributions are two-fold. First, we formulate and solve, using column generation, a new MILP to compute offline energy-throughput tradeoff curve. We use a physical interference model where the nodes can perform continuous power control and can use a discrete set of data rates. Second, we highlight network engineering insights. We show, via numerical results, that power control and multi-rate functionalities allow optimal throughput to be reached, with lower energy consumption, using a mix of single hop and multi-hop routes.

In this paper, we investigate the impact of limited backhaul capacity on user scheduling in the context of a heterogeneous network comprising a macro base station overlaid with small-cells. Under a global proportional fairness (PF) criteria, we show that this limited capacity has a fundamental impact on user scheduling. When user association and channel allocation are given, like in the infinite-capacity backhaul case, the global PF user scheduling problem can be decomposed into a set of independent local PF user scheduling problems. However, unlike the case with infinite backhaul where the local PF scheme is equivalent to giving equal time to each user, a local PF scheme with finite backhaul can be of one of three types. We completely characterize these three types and the conditions under which to use them. The results show that a backhaul-aware scheduling scheme is simple to implement and necessary to obtain the best performance.

Most of the literature on heterogeneous cellular networks is focused on analyzing them as a single macro cell embedded with small cells. In this paper, we take a global perspective and analyze the effect of deploying small cells on the performance of a network comprising several macro cells. We identify potential locations for low-power base-stations based on the coverage patterns of the macro cells and propose three schemes for placing the small cells. Using the model recommended by 3GPP, we show that by judiciously installing just two small cells for every macro base-station at these locations and allocating separate resources to all the small cells on a global level, we can increase the performance of the network significantly (∼ 45%). An added benefit of our schemes is that we can switch off the macro base-stations at night (when the number of active users is low) and significantly reduce their operation cost.

In this paper, we study three scenarios of small-cell deployment in a heterogeneous network comprising a macro base station (MBS) and a set of small cells. The first scenario corresponds to wired backhauling and the remaining two scenarios correspond to two different ways of wireless backhauling. In one of the scenarios of wireless backhauling, the backhaul links have to compete with the user links for LTE channel resources (user-band relay scenario). In the other, an additional (mmWave) band is available exclusively for operating the backhaul links (dedicated-band relay scenario). For each of the scenarios, several different configurations based on channel allocation and node capabilities are considered. We formulate an optimization framework to model, dimension and evaluate all of these configurations. For user-band relay scenario, our results show that some configurations offer either negative or negligible throughput gains over the MBS-only case. By noting that any relay deployment's performance is upper-bounded by wired backhauling with sufficiently large capacity, the results also show that some other configurations offer very good throughput gains, with values very close to the upper-bound. The results highlight the importance of the right choice of configuration to justify the deployment of user-band relay nodes. Further, our results show that, for dedicated-band relay scenario, a small fraction of a typical mmWave bandwidth suffices to yield performance very close to the upper-bound.

The growth of Internet video traffic imposes a severe capacity problem in today's Content Delivery Network (CDN). Rate-adaptive streaming technologies, such as the Dynamic Adaptive Streaming over HTTP (DASH) standard, reinforces this problem in the core CDN infrastructure since delivering one video means delivering multiple representations for an aggregated bit-rate that is commonly over 10 Mbps. In this paper, we explore better trade-offs between CDN infrastructure cost and Quality of Experience (QoE) of the end-users for live broadcast video streaming applications. We consider in particular underprovisioned CDN networks, our goal being to maximize the QoE for the population of heterogeneous end-users despite the lack of resources in the intermediate CDN equipments. We show that previous theoretical models based on elastic bit-rates do not fit for this context. We propose a user-centric discretized streaming model where the satisfaction of end-users is related to the context and where a stream has to be either delivered in its entirety, or not delivered at all. We first formulate an Integer Linear Program (ILP) that achieves the optimal delivery through a multi-tree delivery overlay. The evaluation of the ILP shows the benefits of this model. We then design a practical system by revisiting the three main algorithms implemented in CDN: user-to-server assignment, content placement and content delivery. At last, we use a realistic trace-driven large-scale simulator to study the performances of our system. In particular, we show that the population of users is reasonably well served (three quarters of the population do not experience degradation) even when the CDN infrastructure experiences a severe underprovisioning (less than half of the required infrastructure).

We study cross-layer design in random-access-based fixed wireless multihop networks under a physical interference model. Due to the complexity of the problem, we consider a simple slotted ALOHA medium access control (MAC) protocol for link-layer operation. We formulate a joint routing, access probability, and rate allocation optimization problem to determine the optimal max-min throughput of the flows and the optimal configuration of the routing, access probability, and transmission rate parameters in a slotted ALOHA system. We then also adapt this problem to include an XOR-like network coding without opportunistic listening. Both problems are complex nonlinear and nonconvex. We provide extensive numerical results for both problems for medium-size mesh networks using an iterated optimal search technique. Via numerical and simulation results, we show that: 1) joint design provides a significant throughput gain over a default configuration in slotted-ALOHA-based wireless networks; and 2) the throughput gain obtained by the simple network coding is significant, especially at low transmission power. We also propose simple heuristics to configure slotted-ALOHA-based wireless mesh networks. These heuristics are extensively evaluated via simulation and found to be very efficient.

This work is an offline study to characterize the performance of cooperative relaying in interference-limited multihop networks, where nodes are equipped with multi-rate and continuous power control capabilities. We formulate a cross-layer flow-based framework to obtain the achievable throughput rates by jointly optimizing the parameters for multi-path routing, scheduling, rates, transmit powers, and selection of cooperative nodes. This framework is generic in that it is not restricted to any particular cooperative combining technique or type of network architecture. To take continuous power control into account, we introduce a non-trivial power allocation subproblem while keeping the main cross-layer framework as a linear program. We solve the problem optimally to obtain the max-min throughput for the case when cooperation is based on the distributed Alamouti code and networks have a mesh-like topology. We derive a number of practical engineering insights based on our numerical optimal results obtained for small-to-medium-sized random networks. In particular, we establish that the use of cooperative relaying in a small-to-medium-sized random mesh network often does not yield significant performance gains in throughput and connectivity even when multi-rate and continuous power control capabilities are available at the nodes.

The high variability of solar power due to intrinsic diurnal variability, as well as additional stochastic variations due to cloud cover, have made it difficult for solar farms to participate in electricity markets that require pre-committed constant power generation. We study the use of battery storage to 'firm' solar power, that is, to remove variability so that such a pre-commitment can be made. Due to the high cost of storage, it is necessary to size the battery parsimoniously, choosing the minimum size to meet a certain reliability guarantee. Inspired by recent work that identifies an isomorphism between batteries and network buffers, we introduce a new model for solar power generation that models it as a stochastic traffic source. This permits us to use techniques from the stochastic network calculus to both size storage and to maximize the revenue that a solar farm owner can make from the day-ahead power market. Using a 10-year of recorded solar irradiance, we show that our approach attains 93% of the maximum revenue in a summer day that would have been achieved in daily market had the entire solar irradiance trace been known ahead of time.

For applications in which a node is interested in a function of the data generated at different sources, in-network computation is a promising approach to improve the network performance. In this paper, we study the problem of computing the first M moments of the data using in-network computation in an arbitrary wired communication network. We are interested in finding a routing and queue management strategy that maximizes the data rate at which the sources could generate new data. We first propose a very simple tractable flow model that computes an upper bound on the maximum data generation rate that could be supported in a given network for a given M. To validate the tightness of this upper bound and to provide a practical feasible solution, we then propose a heuristic strategy involving the generation of multiple trees and effective queue management that achieves data generation rates close to this upper bound. This cross-validates the tightness of the upper bound and the goodness of our heuristic strategy. Finally, using the flow model, we provide engineering insights on what in-network computation can achieve.

Many applications require the sink to compute a function of the data collected by the sensors. Instead of sending all the data to the sink, the intermediate nodes could process the data they receive to significantly reduce the volume of traffic transmitted: this is known as in-network computation. Instead of focusing on asymptotic results for large networks as is the current practice, we are interested in explicitly computing the maximum achievable throughput of a given network when the sink is interested in the first M statistical moments of the collected data. Here, the kth statistical moment is defined as the expectation of the kth power of the data. Flow models have been routinely used in multihop wireless networks when there is no in-network computation, and they are typically tractable for relatively large networks. However, deriving such models is not obvious when in-network computation is allowed. We develop a discrete-time model for the real-time network operation and perform two transformations to obtain a flow model that keeps the essence of in-network computation. This gives an upper bound on the maximum achievable throughput. To show its tightness, we derive a numerical lower bound by computing a solution to the discrete-time model based on the solution to the flow model. This lower bound turns out to be close to the upper bound, proving that the flow model is an excellent approximation to the discrete-time model. We then provide several engineering insights on these networks.

We focus on wireless sensor networks (WSNs) that perform data collection with the objective of obtaining the whole dataset at the sink (as opposed to a function of the dataset). In this case, energy-efficient data collection requires the use of data aggregation. Whereas many data aggregation schemes have been investigated, they either compromise the fidelity of the recovered data or require complicated in-network compressions. In this paper, we propose a novel data aggregation scheme that exploits compressed sensing (CS) to achieve both recovery fidelity and energy efficiency in WSNs with arbitrary topology. We make use of diffusion wavelets to find a sparse basis that characterizes the spatial (and temporal) correlations well on arbitrary WSNs, which enables straightforward CS-based data aggregation as well as high-fidelity data recovery at the sink. Based on this scheme, we investigate the minimum-energy compressed data aggregation problem. We first prove its NP-completeness, and then propose a mixed integer programming formulation along with a greedy heuristic to solve it. We evaluate our scheme by extensive simulations on both real datasets and synthetic datasets. We demonstrate that our compressed data aggregation scheme is capable of delivering data to the sink with high fidelity while achieving significant energy saving.

We propose a unified static framework to study the interplay of user association and resource allocation in heterogeneous cellular networks. This framework allows us to compare the performance of three channel allocation strategies: Orthogonal deployment, Co-channel deployment, and Partially Shared deployment. We have formulated joint optimization problems that are non-convex integer programs, are NP-hard, and hence it is difficult to efficiently obtain exact solutions. We have, therefore, developed techniques to obtain upper bounds on the system's performance. We show that these upper bounds are tight by comparing them to feasible solutions. We have used these upper bounds as benchmarks to quantify how well different user association rules and resource allocation schemes perform. Our numerical results indicate that significant gains in throughput are achievable for heterogeneous networks if the right combination of user association and resource allocation is used. Noting the significant impact of the association rule on the performance, we propose a simple association rule that performs much better than all existing user association rules.

In this paper, we formulate a flow-based framework for the joint optimization of resource allocation, transmission coordination, and user association in a heterogeneous network comprising of a macro base station and a set of pico base stations and/or relay nodes. By incorporating these three important network processes together and by unifying the analysis of pico base stations and relay nodes, our framework can act as an important engineering tool for understanding the performance of different configurations. We use the resulting formulations to characterize the performance of different combinations of resource allocation schemes and transmission coordination mechanisms. We obtained important engineering insights regarding the interplay of these network processes. In particular, under the deployment of pico base stations, we find that partially shared deployment outperforms the co-channel deployment, with or without transmission coordination. In contrast, the results also show that the deployment of relay nodes does not offer meaningful throughput gains for any choice of resource allocation scheme or transmission coordination mechanism.

We investigate the impact of cooperative relaying used to create virtual multipoint-to-point links (as opposed to conventional multihop relaying) on the throughput optimal configuration of a wireless network. We achieve this by formulating a cross-layer framework for a joint routing and scheduling problem with cooperative relaying. We consider a general case, where cooperation is allowed between any pair of nodes in a given network. We optimally solve this formulation for max-min throughput in mesh-like networks of medium size and quantify gains for key performance metrics. We establish that, contrary to popular belief, cooperative relaying provides performance gains in a mid-size network surprisingly rarely. Moreover, if gains can be obtained, these gains are typically only marginal. We quantify these gains and provide engineering insights based on numerical results for 200 random realizations of a network with 16 nodes.

This paper presents the design and evaluation of HomeNet Profiler, a tool that runs on an end-system in the home to collect data from home networks. HomeNet Profiler collects a wide range of measurements including: the set of devices, the set of services (with UPnP and Zeroconf), and the characteristics of the WiFi environment. Since the release of HomeNet Profiler in April 2011, we have collected data from over 2,400 distinct homes in 46 different countries.

Current point-to-multipoint systems suffer significant performance losses due to greater attenuation along the signal propagation path at higher frequencies, transmit power constraints of mobile users and base stations, and interference from neighboring cells. Fractional Frequency Reuse (FFR) is a technique to counteract these effects. Typically, the proposed FFR technique partitions a cell into a reuse 1 area, centered near the base-station and a reuse 3 area, located near the edges of the cell, with reuse 3 regions scheduled to minimize interference from neighboring cells. Unfortunately, virtually all analysis of FFR has been done under a perfect hexagonal lattice cellular grid, while no practical deployment has this degree of symmetry. In this paper we revisit the analysis of FFR for non-ideal cellular grids for cases with fading. We find that while for some non-ideal grids, a combination of reuse 1 and 3 is indeed optimal, for many others a combination of reuse 1 and 4 provide better performance. Thus, we conclude that for practical cellular layouts, the optimal re-use pattern for the edge of the cells is not necessarily 3 as commonly assumed, but is topology dependent.

The objective of this paper is to study the impact of advanced physical layer techniques on the maximum achievable throughput of wireless multihop mesh networks. We formulate a cross-layer optimization framework for the routing and scheduling problem jointly with the following physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding and their combinations. In the case when each node is enabled with superposition coding, we need to formulate a power allocation subproblem for the optimal power partition of the superimposed signals. We solve these joint problems exactly to compute the maximum achievable throughput in realistic size networks. This allows us to quantify the performance gains obtained by using these techniques (and their combinations). Specifically, we find that the use of dirty-paper coding (only at the gateway) is not justified in networks with mixed uplink and downlink flows. On the other hand, the combination of superposition coding with successive interference outperforms significantly other techniques across all transmission power range for both uplink and downlink flows. We also provide a number of interesting practical insights on throughput improvement by comparing different combinations of these techniques.

We study the engineering of heterogeneous cellular networks composed of a macrocell and some picocells by investigating the interplay of different network processes and parameters such as channel allocation, user association and reuse pattern (to control inter-cell interference between picocells). We formulate a joint association, channel allocation, and inter-cell interference management problem that relies on very few assumptions. This problem turns out to be an Integer Non-Linear program that is NP-hard. However, its structure is such that we can solve it exactly for relatively large size systems. We use optimal solutions as benchmarks to understand how different simple association schemes perform. Our results show the critical impact of the association rules on system performance and shows the interplay of the different processes and parameters. We believe that these insights will help design online association schemes in the future.

In this paper, a) we investigate a simple throughput based incentive mechanism that could convince home users to install residential femtocells (by offering them a rate which is α > 1 times higher than the average rate received by a user associated with the macro base station), avoid most of the curse of free riders (i.e., selfish users who gets improved service just because others are investing in femto technology) and, at the end, be beneficial to both the users and the operator, and b) we propose a heuristic to compute the resource allocation supporting this incentive scheme. We show that it is cost-effective for a network operator to allocate a pool K of its licensed channels to a certain number of femto cells, even under our incentive mechanism. We first study a static scenario with fixed numbers of macro and femto users. We identify the range of values of the incentive parameter α that can be supported to reward femto users without harming macro users and we quantify the throughput gains for both macro and femto users in different situations. Then, we consider a dynamic scenario, where the number of users and the number of active femtocells change continuously, and we propose a simple and very accurate heuristic for computing K with a minimal amount of information to be collected at the macro base station. Our numerical results show that by using our proposed approach both macro and femto users are better off than in a pure macrocellular scenario.

This paper presents a study on achievable throughput in wireless multihop networks with unicast flows that use XOR-like network coding. A joint routing, scheduling, and network coding problem is formulated under a realistic signal to interference plus noise ratio interference model. This formulation provides a mathematical framework to study the achievable throughput of a given wireless network for a given utility function. We optimally solve it for max-min throughput in small to medium size networks by developing an efficient computation tool. Our numerical results show that throughput gains can be obtained at low transmission powers by using simple XOR-like network coding in a mesh-like network provided it is optimally configured in terms of routing, scheduling, and network coding but that they are only significant (i.e., greater than 15%) for some special cases. We also compute max-min throughput by restricting network coding to some key nodes or flows to quantify key conditions that provide a significant portion of gains.

High throughput and lifetime are both crucial design objectives for a number of multihop wireless network applications. As these two objectives are often in conflict with each other, it naturally becomes important to identify the trade-offs between them. Several works in the literature have focused on improving one or the other, but investigating the trade-off between throughput and lifetime has received relatively less attention. We study this trade-off between the network throughput and lifetime for the case of fixed wireless networks, where link transmissions are coordinated to be conflict-free. We employ a realistic interference model based on the Signal-to-Interference-and-Noise Ratio (SINR), which is usually considered statistically sufficient to infer success or failure of wireless transmissions. Our analytical and numerical results provide several insights into the interplay between throughput, lifetime, and transmit power. Specifically, we find that with a fixed throughput requirement, lifetime is not monotonic with power-neither very low power nor very high power result in the best lifetime. We also find that, for a fixed transmit power, relaxing the throughput requirement may result in a more than proportional improvement in the lifetime for small enough relaxation factors. Taken together, our insights call for a careful balancing of objectives when designing a wireless network for high throughput and lifetime.

Although compressed sensing (CS) has been envisioned as a useful technique to improve the performance of wireless sensor networks (WSNs), it is still not very clear how exactly it will be applied and how big the improvements will be. In this paper, we propose two different ways (plain-CS and hybrid-CS) of applying CS to WSNs at the networking layer, in the form of a particular data aggregation mechanism. We formulate three flow-based optimization problems to compute the throughput of the non-CS, plain-CS, and hybrid-CS schemes. We provide the exact solution to the first problem corresponding to the non-CS case and lower bounds for the cases with CS. Our preliminary numerical results are only for a low-power regime. They illustrate two crucial insights: first, applying CS naively may not bring any improvement, and secondly, our hybrid-CS can achieve significant improvement in throughput.

We present a number of significant engineering insights on what makes a good configuration for medium- to large-size wireless mesh networks (WMNs) when the objective function is to maximize the minimum throughput among all flows. For this, we first develop efficient and exact computational tools using column generation with greedy pricing that allow us to compute exact solutions for networks significantly larger than what has been possible so far. We also develop very fast approximations that compute nearly optimal solutions for even larger cases. Finally, we adapt our tools to the case of proportional fairness and show that the engineering insights are very similar.

In this paper we consider resource allocation for an OFDMA-based cognitive radio point-to-multipoint network with fixed users. Specifically, we assume that secondary users are allowed to transmit on any subchannel provided that the interference that is created to any primary users is below a critical threshold. We focus on the downlink. We formulate the joint subchannel, power and rate allocation problem in the context of finite queue backlogs with a total power constraint at the base station. Thus, users with small backlogs are only allocated sufficient resources to support their backlogs while users with large backlogs share the remaining resources in a fair and efficient fashion. Specifically, we formulate the problem as a max-min problem that is queue-aware, i.e., on a frame basis. We maximize the smallest rate of any user whose backlog cannot be fully transmitted. While the problem is a large non-linear integer program, we propose an iterative method that can solve it exactly as a sequence of linear integer programs, which provides a benchmark against which to compare fast heuristics. We consider two classes of heuristics. The first is an adaptation of a class of multi-step heuristics that decouples the power and rate allocation problem from the subchannel allocation and is commonly found in the literature. To make this class of heuristics more efficient we propose an additional (final) step. The second is a novel approach, called selective greedy, that does not perform any decoupling. We find that while the multi-step heuristic does well in the non-cognitive setting, this is not always the case in the cognitive setting and the second heuristic shows significant improvement at reduced complexity compared to the multi-step approach. Finally, we also study the influence of system parameters such as number of primary users and critical interference threshold on secondary network performance and provide some valuable insights on the operation of such systems.

In this paper, we study the joint configuration of routing and medium access control (MAC) parameters in fixed wireless networks. Due to the complexity of the problem, we consider a simple slotted ALOHA MAC protocol for link layer operation. We model the link rate of the slotted ALOHA system under a saturation assumption and use a signal to interference plus noise ratio (SINR) based interference model via the concept of conflict set. We formulate a joint routing and MAC (JRM) optimization problem to determine the optimal max-min throughput of the flows and the optimal configuration of routing and MAC parameters. The JRM optimization problem is a non-convex optimization problem and we solve it by an iterated optimal search technique. We validate our approach via simulation and illustrate the potentially high throughput gains that can be obtained by using our joint configuration technique.

We investigate downlink resource allocation for OFDM-based cognitive radio networks. It is assumed that secondary users are allowed to transmit on all subchannels as long as the interference they create for primary users remains below a critical threshold. We consider a practical setting where secondary users have finite queue backlogs and a total power constraint at the base station and we perform resource allocation either over one or multiple time slots. Specifically, secondary users with small queue backlogs are only allocated sufficient rates to support their traffic demands and the remaining radio resources are shared among highly backlogged users. Under this setting, we formulate the joint subchannel and power problem with max-min fairness for highly backlogged users. Then, we propose an iterative procedure to find an optimal resource allocation solution using an integer program solver. For online implementation, we develop several heuristics of increasing complexity and performance. Numerical results show that the proposed heuristics achieve very good performance compared to the optimal solutions and that taking queue backlogs into account does not make the heuristics much slower while making the system more responsive to users' need.

We focus on efficient algorithms for resource allocation problems in large wireless networks. We first investigate the link scheduling problem and identify the properties that make it possible to compute solutions efficiently. We then show that the node on-off scheduling problem shares these features and is amenable to the same type of solution method. Numerical results confirm the efficiency of our technique for large scale problems. We also extend the technique to the case where the objective function is nonlinear showing that our technique blends smoothly with a sequential linear programming approach. Numerical results for a cross-layer design with a nonlinear fairness utility show that it is possible to compute optimal solutions for large wireless networks in reasonable CPU time.

We consider the problem of determining asymptotic bounds on the capacity of a randomadhocnetwork. Previous approaches assumed a link layer model in which if a transmitter-receiver pair can communicate with each other, i.e., the signal to interference and noise ratio (SINR) is above a certain threshold, then the transmitted packet is received error-free by the receiver thereby. Using this model, the per node capacity of the network was shown to be Theta(radic(nlogn)/1). In reality, for any finite link SINR, there is a nonzero probability of erroneous reception of the packet. We show that in a large network, as the packet travels an asymptotically large number of hops from source to destination, the cumulative impact of packet losses over intermediate links results in a per-node throughput of onlyO(radic(n)/1) under the previously proposed routing and scheduling strategy. We then propose a new scheduling scheme to counter this effect. The proposed scheme provides tight guarantees on end-to-end packet loss probability, and improves the per-node throughput to Omega(radic(n)(logn)/12(alpha-2)/alpha+2) wherealpha>2is the path loss exponent.

In wireless communications, the desired wireless signal is typically decoded by treating the sum of all the other ongoing signal transmissions as noise. In the networking literature, this phenomenon is typically abstracted using a wireless channel interference model. The level of detail in the interference model, evidently determines the accuracy of the results based upon the model. Several works in the networking literature have made use of simplistic interference models, e.g., fixed ranges for communication and interference, the capture threshold model (used in the ns2 network simulator), the protocol model, and so on. At the same time, fairly complex interference models such as those based on the SINR (signal-to-interference-and-noise ratio) have also been proposed and used. We investigate the impact of the choice of the interference model, on the conclusions that can be drawn regarding the performance of wireless networks, by comparing different wireless interference models. We find that both in the case of random access networks, as well as in the case of scheduled networks (where node transmissions are scheduled to be completely conflict-free), different interference models can produce significantly different results. Therefore, a lot of caution should be exercised before accepting or interpreting results based on simplified interference models. Further, we feel that an SINR-based model is the minimum level of detail that should be employed to model wireless channel interference in a networking context.

We report in this paper measurements from France Telecom commercial networks carrying traffic generated and received by ADSL and FTTH customers. By adopting a flow- based approach to traffic analysis, we show that both types of customers experience similar peer-to-peer services in that the bit rates that they see is rather low. In order to understand the origin of these similarities, we develop a mathematical model, which could be seen as an abstraction of a file sharing process between peers according to the principles of eDonkey. This model allows us to exhibit a phase transition phenomenon which is nested in the file sharing principle. We believe that this phenomenon explains why both types of customers see a congested peer-to- peer network.

Wireless mesh networks are considered as a potentially attractive alternative to provide broadband access to users. They have been studied extensively by the research community since they raised a lot of new issues due to their unique characteristics. Here, we focus on scenarios where these networks are installed and managed to provide broadband access to a set of fixed nodes. While a lot of research has been done on this type of networks, there are very few insightful engineering results that can help network operators deploy and manage such networks. It is the objective of this paper to present some major engineering insights on such networks. We limit our scope to networks that are single rate and in which all nodes use the same transmit power. In particular, we quantify the advantage of multi-hop over single-hop. We illustrate the importance of multi-path routing over single path routing, and of optimal routing versus min-hop routing. We revisit the notion of spatial reuse. Finally we present results showing the importance of selecting an appropriate interference model.

We consider a resource allocation problem for spectrum sharing in cognitive radio networks. Specifically, we investigate the joint subchannel, rate and power allocation for secondary users which share, in a non-disruptive manner, some frequency bands with primary users using OFDM technology. We consider the resource allocation problem for downlink and take into account the maximum total power constraints of the base station and the power constraints determined by distributed spectrum sensing and scanning. We formulate a resource allocation problem as an optimization problem which achieves max-min rate sharing among users. We propose both integer program based optimal and suboptimal fast and low complexity approaches for the spectrum sharing problem. Numerical results are then presented for the proposed heuristics and compared with the optimal solution.

Scheduled wireless mesh networks (WMNs) represent an important paradigm in the development of high speed wireless access networks. As a consequence of (Aditya Karnik, 2006), it can be shown that the maximum throughput of a single gateway n node WMN is upper-bounded by 1/n, normalized with respect to the highest available data-rate. The situation is actually worse since with conventional omni-directional antennas, the achievable throughput is considerably lower at low powers. This paper makes a case for smart (directional) antennas. For the same power, smart antennas can provide higher range, and therefore shorter paths, as compared to omni-directional antennas. For the same range, smart antennas can operate at lower powers, reducing the interference, and thereby improving the spatial reuse. Although they cannot change the fundamental throughput limit of 1/n, smart antennas can achieve significantly higher throughput at lower powers, These insights are demonstrated through several numerical examples using the framework of (Aditya Karnik, 2006).

The problem of determining asymptotic bounds on the capacity of a random ad hoc network is considered. Previous approaches assumed a threshold-based link layer model in which a packet transmission is successful if the SINR at the receiver is greater than a fixed threshold. In reality, the mapping from SINR to packet success probability is continuous. Hence, over each hop, for every finite SINR, there is a non-zero probability of packet loss. With this more realistic link model, it is shown that for a broad class of routing and scheduling schemes, a fixed fraction of hops on each route have a fixed non-zero packet loss probability. In a large network, a packet travels an asymptotically large number of hops from source to destination. Consequently, it is shown that the cumulative effect of per-hop packet loss results in a per-node throughput of only O (1/n) (instead of thetas[1/radic(n log n)] as shown previously for the threshold-based link model)

In a wireline network, nodes form links with only those nodes they are wired to, and the links do not interfere with one another. In contrast, in a wireless network, signal transmissions are intrinsically broadcast, and suffer from mutual interference. In several physical layer technologies, a wireless signal is decoded by treating the sum of all the other on-going signal transmissions as noise. Hence, from a networking standpoint, there is a need to model wireless channel interference. An accurate interference model is especially important in a multi-hop network context, since there could be several simultaneous wireless transmissions. Several works in the literature have made use of simplified interference models. Some works assume a fixed range for communication and interference, while others are based on concepts like capture threshold where the desired signal strength is compared with interference from a single node at a time, rather than cumulatively. In particular, the latter model is used in ns2 which is the most common simulation tool. Under isotropic pathloss, the capture threshold model is also equivalent to the protocol model proposed by Gupta and Kumar, which is now the subject of a lot of analytical activity notably through conflict graph based problem formulations. We investigate the accuracy and appropriateness of the capture threshold based interference model, by comparing it with one based on the SINR (signal-to-interference-and-noise ratio) with additive interference calculation. We find that both in the case of random access networks, as well as in the case of scheduled networks (where node transmissions are scheduled to be completely conflict-free), a simplified interference model such as the capture threshold model, can produce significantly different results compared to an additive interference based model. Therefore, a lot of caution should be exercised before accepting or interpreting results based on simplified interference models.

Minimizing energy consumption is crucial for portable wireless stations because they operate on a limited battery supply. The mechanism called power-saving mode (PSM) allows a network interface on a wireless station to enter the sleep mode whenever possible to reduce its energy consumption. At the same time, there has been a growing popularity in multi-rate wireless systems that can exploit the time-varying nature of the radio environment to increase the overall performance of the system under certain QoS/fairness requirements of users. The primary objective in these so-called systems is to increase the system throughput by giving priority to a mobile station experiencing better channel condition. In this work, we propose an opportunistic Power-Saving Mode and a corresponding scheduler design for wireless local area networks, which improves the energy consumption of PSM stations while maintaining throughput maximization, by exploiting time-varying channel conditions. We identify the challenges in the design and implementation of the PSM and the scheduler. We design a channel probing scheme and a scheduler called OEES (Opportunistic Energy Efficient Scheduler), which considers throughput maximization first and then focuses on minimizing energy consumption. Extensive simulations show that our scheme saves a significant amount of energy while maintaining throughput maximization.

We study the impact of network load in the neighboring sectors on the inter-cell interference in a cellular data network. The signal received by a user over the forward link in such a system contains interference from the neighboring base stations. We note that the strength of this interference is a function of the network load in the neighboring cells. We obtain an expression for the received SINR (signal to interference and noise ratio) as a function of the traffic load in the interfering cells. Using this result, we propose an improvement to the conservative pilot-based SINR estimation scheme that is implemented in the current cellular data networks. The proposed scheme provides a more accurate estimate of the user SINR by taking better account of the contribution of inter-cell interference. It builds on top of the current SINR measurement scheme by using a combination of pilot measurement and traffic load measurement. With the proposed scheme, a terminal reports a less conservative data rate, and hence it receives a higher throughput. The scheme especially benefits the "poor" users, i.e., the users that receive low throughput because they are located far from the base station. For example, when the network load in the neighboring sectors is 0.5, with the proposed scheme, the throughput received by a single vehicular user located about three-fourth of the way between the serving base station and the cell boundary, is about 35% higher than the throughput obtained using the scheme that is used in current practice

Abstract Multi‐hop wireless networks arise in the context of ad hoc networks, sensor networks, and mesh networks, and their performance depends critically on the underlying medium access control (MAC) protocol. Inspite of the large body of work devoted to MAC protocols and associated problems, the relative importance of these problems is still not well understood. This is because most of the previous work focuses on designing a protocol to solve a particular problem, or on identifying scenarios where a protocol will not work well. In addition, most of the work is also based on simplistic assumptions about the physical wireless medium, like fixed ranges for communication and interference, or concepts like capture threshold where the desired signal strength is compared with interference from a single node at a time, rather than cumulatively. Our paper seeks to address these issues. We believe it is extremely critical that (i) we develop an understanding of the relative significance of the problems affecting MAC protocols, and that (ii) we use a realistic model for the physical channel for design and performance evaluation. Towards this end, we evaluate the performance of three currently proposed MAC protocols, IEEE 802.11 [1], RI‐BTMA [2], and DUCHA [3] under a realistic channel model with additive interference. Since these protocols solve or suffer from different sets of problems, our evaluation provides a differential diagnosis of the severity of these problems. Based on our observations, we propose a simple and robust two channel MAC protocol (entitled 2CM) that is based on IEEE 802.11 augmented with a busy‐tone channel. The 2CM protocol (i) mitigates the hidden node problem considerably, (ii) does not waste bandwidth in terms of logical control channels, and (iii) provides a reliable link layer acknowledgment. Through extensive simulations, we show that 2CM offers a consistently high throughput performance while not sacrificing link layer reliability in a variety of scenarios, thereby vindicating our approach. Copyright © 2006 John Wiley & Sons, Ltd.

Wireless networks are characterized by the broadcast nature of the wireless channel, strong path loss, time varying fading and shadowing, and limited battery and processing power of the devices. These properties of the wireless physical layer interact in a complex manner with the higher layers of the protocol stack (MAC and routing), and present several interesting challenges when analyzing, dimensioning and designing wireless networks. The focus of this presentation is on discussing these cross-layer interactions, and the design of schemes which take advantage of these interactions.We will draw examples from different scenarios including wireless sensor networks, ad hoc networks and cellular networks.Joint work with Vivek Mhatre, Sunil Kulkarni, and Aravind Iyer.

Various MAC protocols have been suggested for exploiting the possible benefits provided by directional antennas, but most of the solutions suggested are either based on the concept of sending (MAC level) control packets (such as RTS and CTS) in all directions, or based on the use of some omnidirectional control packet transmissions. We believe that these approaches fail short of exploiting the capability of directional transmission/reception fully and are incapable of handling all the problems resulting from the use of directional antennas in multi-hop wireless networks. Hence we first propose a set of requirements that should be met by a good MAC protocol that uses directional antennas. Then we propose a directional busy signal multiple access (DBSMA) protocol that meets these requirements. In DBSMA, all the transmissions, receptions, and idle listening are directional. The need to listen in many directions when in an idle state is achieved by constantly changing the listen direction to cover the whole space. We also propose a novel directional back-off mechanism in which every node maintains independent back-off windows for each direction and show how it yields better performance. We show that DBSMA is well suited for ad-hoc multi-hop networks with directional antennas

We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed over the area using two-dimensional homogeneous Poisson point processes; type 0 nodes with intensity (average number per unit area) /spl lambda//sub 0/ and battery energy E/sub 0/; and type 1 nodes with intensity /spl lambda//sub 1/ and battery energy E/sub 1/. Type 0 nodes do the sensing while type 1 nodes act as the cluster heads besides doing the sensing. Nodes use multihopping to communicate with their closest cluster heads. We determine them optimum node intensities (/spl lambda//sub 0/, /spl lambda//sub 1/) and node energies (E/sub 0/, E/sub 1/) that guarantee a lifetime of at least T units, while ensuring connectivity and coverage of the surveillance area with a high probability. We minimize the overall cost of the network under these constraints. Lifetime is defined as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are also taken into account, i.e., we ensure that almost all the nodes run out of energy at about the same time so that there is very little energy waste due to residual energy. We compare the results for random deployment with those of a grid deployment in which nodes are placed deterministically along grid points. We observe that in both cases /spl lambda//sub 1/ scales approximately as /spl radic/(/spl lambda//sub 0/). Our results can be directly extended to take into account unreliable nodes.

The problem of connectivity in multi-hop wireless networks has been extensively studied in the literature and general results for node density requirements have been obtained (P. Gupta and P.R. Kumar, Proc. 37th IEEE Conf. of Decision and Control, 1998). These results have been obtained based on the implicit assumption of a generic routing protocol, capable of exhaustively searching all possible routes, between all pairs of nodes. Hence these results would be too optimistic in several practical cases, where the routing protocols are not generic but optimized for specific applications. In this paper, we provide a framework for defining the appropriate notion of connectivity that reflects the underlying network architecture and protocols. Based on this framework, we define and analyze connectivity requirements for two network architectures proposed in the literature, namely, GAF (geographic adaptive fidelity) with Manhattan routing (Y. Xu et al., Proc. Int. Conf. on Mobile Comp. and Netw., 2001), proposed for ad-hoc networks and AIMRP (address-light integrated MAC and routing protocol) which employs tier-based routing in sensor networks (S.S. Kulkarni et al., submitted for publication in IEEE Trans. on Networking, Dec. 2003). By comparing the critical node density requirements for connectivity, obtained through our framework, with the results in Gupta and Kumar, we show that the earlier results are too optimistic and hence it is important to consider the underlying routing protocol to dimension the density of nodes appropriately.

This paper presents a cost based comparative study of homogeneous and heterogeneous clustered sensor networks. We focus on the case where the base station is remotely located and the sensor nodes are not mobile. Since we are concerned with the overall network dimensioning problem, we take into account the manufacturing cost of the hardware as well as the battery energy of the nodes. A homogeneous sensor network consists of identical nodes, while a heterogeneous sensor network consists of two or more types of nodes (organized into hierarchical clusters). We first consider single hop clustered sensor networks (nodes use single hopping to reach the cluster heads). We use LEACH as the representative single hop homogeneous network, and a sensor network with two types of nodes as a representative single hop heterogeneous network. For multihop homogeneous networks (nodes use multihopping to reach the cluster head), we propose and analyze a multihop variant of LEACH that we call M-LEACH. We show that M-LEACH has better energy efficiency than LEACH in many cases. We then compare the cost of multihop clustered sensor networks with M-LEACH as the representative homogeneous network, and a sensor network with two types of nodes (that use in-cluster multi-hopping) as the representative heterogeneous network.

This paper proposes to study the impact of a suite of policies on the performance of a multifile distribution system that integrates CDN and P2P techniques. One of the policies is the peer contribution policy that decides the limited data rate and data volume to be contributed by each peer. The peer contribution policy is critical to maintaining the system's overall file distribution capacity without unfairly overloading the individual peers. In our previous work, an analytical framework for the modeling of a hybrid CDN-P2P architecture under a file-specific peer contribution policy is presented. In this paper, we focus on a different scenario where multiple files are being distributed and the peer contribution policy is file-independent. We argue that suites of policies need to be studied, in order to understand their impacts on the overall file distribution performance. The policies include: (1) file-independent peer contribution policy, (2) file request admission policy, (3) supplier selection policy, and (4) file replacement policy. We define a system model for the analysis of these policies. Based on the model, we also propose possible definitions of the policies.

Understanding the nature of the information flowing into and out of a system or network is fundamental to determining if there is adherence to a usage policy. Traditional methods of determining traffic type rely on the port label carried in the packet header. This method can fail, however, in the presence of proxy servers that remap port numbers or host services that have been compromised to act as backdoors or covert channels. We present an approach to classify server traffic based on decision trees learned during a training phase. The trees are constructed from traffic described using a set of features we designed to capture stream behavior. Because our classification of the traffic type is independent of port label, it provides a more accurate classification in the presence of malicious activity. An empirical evaluation illustrates that models of both aggregate protocol behavior and host-specific protocol behavior obtain classification accuracies ranging from 82-100%.

We consider a scheduling problem for packet based wireless systems with time-varying channel conditions. Designing scheduling mechanisms that take advantage of time-varying channel conditions, which are different for different users, is necessary to improve the wireless system performance. Such scheduling mechanisms are called opportunistic. In this paper we formulate an opportunistic scheduling problem with short term processor sharing fairness constraints as an optimization problem where short term refers to the time window on which the fairness is guaranteed. In its most general form, this problem cannot be solved analytically. We first solve the above optimization problem for three special cases. We consider the scheduling problem with long term fairness constraints; then we consider the scheduling problem for the shortest possible window under two sets of assumptions namely, one in which users have identically distributed channel conditions and another in which users have independent channel conditions. Observing the form of the corresponding optimal policies, we define a heuristic policy for our original opportunistic scheduling problem with short term fairness constraints. We show via simulation that our heuristic policy attains a good trade-off by guaranteeing short term fairness while achieving high average system throughput. We also illustrate that the optimal opportunistic scheduling policy with long term fairness constraint is in fact unfair in practical scenarios.

This paper addresses end-to-end connectivity planning and call admission control for a high capacity multi-beam satellite network with on-board cross-connectivity. On board satellite switching is a technology designed to offer multimedia services, especially in demographically dispersed areas. Nevertheless, full on-board switching techniques are far from maturity. Their implementations have been proven expensive and difficult. There are also high risks involved in launching satellites for the stationary orbit surrounding the earth. As a substitute, a satellite network with on-board cross-connect is devised in this paper. Connectivity planning and call admission control mechanisms associated with such a network are also presented. Simulation studies are conducted to show the effectiveness of the proposed mechanisms.

We consider a scheduling problem for packet based systems with time-varying channel conditions. Designing scheduling mechanisms that take advantage of time-varying channel conditions, which are different for different users, is necessary to improve system performance; however this has to be done in a way that provides some level of fairness among the users. Such scheduling mechanisms are termed opportunistic. We generalize the opportunistic scheduling mechanisms in the literature on two fronts. First, we formulate and solve an opportunistic scheduling problem with multiple general long term QoS constraints and a general system objective function. We call the solution of this opportunistic scheduling problem a generalized water filling solution. Then, we generalize this problem to include multiple interface systems in which several users can be served simultaneously. Apart from the long term QoS constraints specified by each user, multiple interface systems are constrained with other physical limitations imposed by the system. Our main contribution is to show that the structure of the optimal opportunistic scheduling policy is carried over to the problem with general constraints and multiple interfaces. We also study the stability of the multiple interface systems and propose a throughput optimal scheduling rule for such systems.

In our previous work, we have designed, using a network-based approach, and are currently deploying on Purdue wireless infrastructure, a Web service for location discovery of 802.11-based mobile devices. This paper presents a novel web-based application called remote printing service (RPS) which is entirely built on top of our location discovery service (LODS). RPS is not only capable of locating the nearest printers but also allows mobile users to print without having to install any printer drivers. Most of the time users can print even without having to download the file to the mobile device. We have also designed a prototype for a network-based personal paging system that provides active paging and active email notification capability for mobile users. These value-added wireless services contribute to the building and promotion of an e-campus community.

In this paper, we present simple performance bounds for multiplexed regulated traffic streams, which are leaky-bucket regulated with peak, mean rate and burst size constraints. We consider independent, heterogeneous streams, which are multiplexed in a common buffer. We derive bounds on the mean delay in the deterministic context and we then obtain a simple stochastic bound, which is exact when the number of sources increases. A byproduct is a characterization of the worst case sources for mean delay, when they are leaky bucket regulated.

We consider the problem of multirate network design with point-to-multipoint communications. We give a mathematical formulation for this problem. Using approximations, we show that traffic concentration on a small number of links significantly reduces the cost of the network. We then propose a heuristic based on the traffic concentration principle to solve the network design problem approximately. Because this heuristic no longer requires advanced knowledge of demand matrices, we explain how it can be used as the basis for real-time design procedures. By means of numerical results, we show that this heuristic yields nearly optimal solutions.

The Nash arbitration scheme from cooperative game theory provides a natural framework to address the allocation of available bandwidth in network links which is network (Pareto) optimal and satisfies precise notions of fairness. In this paper we propose two distributed bandwidth allocation schemes that allocate available bandwidths to elastic sources according to the Nash arbitration scheme. We prove convergence to the desired allocations for both algorithms. Finally we show how such a scheme can be implemented in a real network.

In this paper, we present a game theoretic framework for bandwidth allocation for elastic services in high-speed networks. The framework is based on the idea of the Nash bargaining solution from cooperative game theory, which not only provides the rate settings of users that are Pareto optimal from the point of view of the whole system, but are also consistent with the fairness axioms of game theory. We first consider the centralized problem and then show that this procedure can be decentralized so that greedy optimization by users yields the system optimal bandwidth allocations. We propose a distributed algorithm for implementing the optimal and fair bandwidth allocation and provide conditions for its convergence. The paper concludes with the pricing of elastic connections based on users' bandwidth requirements and users' budget. We show that the above bargaining framework can be used to characterize a rate allocation and a pricing policy which takes into account users' budget in a fair way and such that the total network revenue is maximized.

A major challenge in the development of integrated service packet networks is to devise mechanisms that can support services with different QoS requirements. Recently, a new mechanism was developed, called weighted fair blocking (WFB), that can be placed on top of almost any discrete-time multiplexing system for controlling packet loss. The WFB mechanism has a number of attractive features. It can offer sources a range of QoS loss levels or be used as a balanced fair blocking (BFB) mechanism to equalize blocking probabilities in the interest of fairness. The BFB mechanism also reduces multiplexer buffer requirements significantly. The mechanism was derived for multiple Bernoulli sources as well as for two bursty MMBP (Markov modulated Bernoulli processes) sources. In this paper, we extend the BFB mechanism to the more realistic case of multiple MMBPs. The extension is found to be theoretically tractable but significant practical complications arise. To circumvent these difficulties, a simple heuristic BFB (H-BFB) mechanism is proposed. The performance of H-BFB is found to be remarkably good in approaching that of theoretically exact BFB from the point of view of buffer gains and fairness.

In this paper we study the cell loss asymptotics for finite buffers accessed by a large number of stationary independent sources and which are served according to a strict HOL priority rule. We first consider the case of two buffers with one of them having strict priority over the other and we obtain asymptotically exact expressions for the cell loss probability for the queues. The asymptotics are studied in terms of a scaling parameter which reflects the server speed, buffer size and the number of sources in such a way that the ratios remain constant. Moreover, as in the single queue case the results are valid for long-range dependent sources with bounded instantaneous rates. The results are then generalised to the case of M buffers where it is shown that resource pooling takes place by which all higher order priority queues can be lumped together when calculating the asymptotics of the lowest priority queue. We conclude with some numerical validation of our formulae against simulations which confirm the theory.

A major challenge in integrated services packet networks is the design of packet multiplexing systems to support different QoS requirements. We focus on the problem of satisfying different loss requirements. In time-slotted multiplexing systems, one may have batch arrivals (i.e., simultaneous arrivals in a time slot), and it is necessary to reject some of the packets in a batch when there is an insufficient number of available buffers. We propose a simple and intelligent probabilistic mechanism for rejecting packets in order to meet the loss requirements. It may be used as a selective discard mechanism to provide different QoS levels in terms of loss (weighted fair blocking). It may also be used to achieve fairness (balanced fair blocking). It offers important advantages and gains relative to existing approaches while being very simple. It allows decoupling of buffer dimensioning from the parameterization of a discard mechanism. It is universal in the sense that it may be used on top of many discrete-time multiplexing schemes. It also reduces the buffer space needed to meet loss requirements compared to natural rejection methods such as random selection. The mechanism is derived explicitly for two Markov modulated Bernoulli sources. The derivation for an arbitrary number of Bernoulli sources is formulated as a linear programming problem.

The cell loss priority (CLP) bit in the header of the ATM cell may be used either by the network to tag noncompliant cells, or by the application to declare two levels of quality-of-service (QoS) within the same virtual circuit (VC). We study the possibility of the use of this bit by the application alone. An application can offer two types of traffic streams to the network, namely, a precious traffic stream (with stringent QoS requirements, e.g., cell loss ratio (CLR) <10/sup -9/ and identified by the CLP bit=0) and a less precious stream (CLP=1 and less stringent QoS requirements, e.g., CLR <10/sup -4/). We study the performance of an ATM multiplexer with two traffic classes with different QoS requirements. The buffer priority schemes adopted are partial buffer sharing (PBS) and PBS+push-out (PO). We first obtain the engineering trade-off curves, between CLP=0 and CLP=1 traffic. To identify an operating point, we formulate a revenue optimization problem in which the constraints are the engineering trade-off curve and a simple model of the variation of CLP=1 demand with its price.

Provides a heuristic framework for the analysis and design of source policing mechanisms for ATM networks. The authors first introduce a function called the time /spl epsiv/-quantile function associated with a source and a second function associated with the policing mechanism. These two characteristics when used in conjunction allow to obtain a useful design tool for source policing. The authors illustrate the use of this framework on source models as well as on real video sources. This framework leads also to a new and natural notion of the response time (called detection time) of the policing mechanism and show how the heuristic framework allows for a quick estimation of this detection time.>

A model for node-to-node blocking probability evaluation in loss networks with state-dependent routing minimizing the reward from carried calls is synthesized. The model is based on the link independence assumption and results in an extended set of fixed point equations solved by repeated substitutions. The policy maximizing the reward from the network and the network performance under this policy are evaluated at the same time. The extensive numerical study covers the issues of accuracy, convergence, and control of performance allocation among different call classes.>

The authors highlight the fact that most of the policing schemes proposed in the literature neglect important information on the past cell blocking behavior of the policing mechanism. They propose a modification of these schemes in which the discarded cell information is used, with almost no overhead compared to the usual strategies. The behavior of the modified policing mechanisms is discussed. This new behavior is illustrated by using the generalized leaky bucket to police the mean rate of a source. Analysis using an on/off input process shows that the modified versions detect smaller-magnitude abuses, police the sources closer to their declared resources utilization, and have a faster response time. Results are presented showing that the generalized sliding window exhibits the same kind of behavior.>