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This is Part I of a two-part paper series that studies the use of the proportional fairness (PF) utility function as the basis for resource allocation and scheduling in multi-channel multi-rate wireless networks. The contributions of Part I are threefold. (i) We present the fundamental properties and physical/economic interpretation of PF optimalit...
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... formulation in (1) is quite general and applies to re- source allocation and scheduling problems in various settings. Part I focuses on the application domain of large-scale wireless local area networks (WLAN), as illustrated in Fig. 1. There are U wireless stations (STA) and S wireless access points (AP) distributed over a geographical region. Suppose that adjacent APs operate on different frequency channels so that there is no co-channel interference among the WLANs. Then, essentially we have S channels in the system. The data transmission rate enjoyed by STA i if ...
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... in multi-channel wireless networks. Section III gives several characteristics of PF optimal solutions useful for the construction of PF algorithms and interpretation of numerical results later. Section IV presents several PF algorithms. Section V makes use of one of the algorithms to generate numerical results for the application scenario of Fig. 1. Section V concludes Part I of the paper ...
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... 2: There is an optimal solution with no more than min(U, S − 1) users using more than one channel in the overall system, and with at least max(0, U − S + 1) users using just one channel. Comment: With respect to the AP allocation problem in Fig. 1, to the extent that there are many more STAs than APs, Corollary 2 basically says that most STAs will associate with only one AP. Proof of Corollary 2: Obvious from Theorem 4. Proof of Theorem 4: See Appendix ...
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... points for the numerical study in the next section virtually impossible. In contrast, the computational time is quite manageable with the above U -user-S-channel algorithm, even for a non-parallel version. It typically takes around 0.6 seconds to converge when there are, for instance, 16 APs and 64 mobile stations in the WiFi network shown in Fig. 1. With this kind of time scale, the algorithm is also suitable for actual field deployment beyond mere numerical studies, since AP allocation and re-allocation are usually not invoked in a frequent manner in typical WLAN-usage scenarios where the users are not highly ...
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... now consider the resource allocation problem in WiFi networks with multiple adjacent WLANs (see Fig. 1). In this study, we assume that there are 16 APs being placed in a square grid. The adjacent APs are separated by 20 meters. A wrap-around method is applied to create a torus topology to eliminate the edge effect: i.e., the rightmost column (top row) is adjacent to the leftmost column (bottom row). A mobile station can transmit at ...
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... Since the signal strength fluctuates independently for different stations, this strategy effectively exploits multi-user diversity. This is achieved by maximizing the sum of logarithmic throughput cost function of the individual stations [141][142][143], i.e., L ℓ=1 logR ℓ , whereR ℓ is defined in (3.9). ...
The evolution of wireless communication networks, from present to the emerging fifth-generation (5G) new radio (NR), and sixth-generation (6G) is inevitable, yet propitious. The thesis evolves around application of machine learning and optimization techniques to problems in spectrum management, internet-of-things (IoT), physical layer security, and intelligent reflecting surface (IRS). The first problem explores License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network as a promising technique to address the spectrum scarcity issue in wireless networks. An optimal communication policy is devised which maximizes the throughput performance of LAA network while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The numerical results demonstrate more than 75 % improvement in the LAA throughput and a notable gain of 8-9 % in the fairness index. Next, we investigate the unlicensed spectrum sharing for bandwidth hungry diverse IoT networks in 5G NR. An efficient coexistence mechanism based on the idea of adaptive initial sensing duration (ISD) is proposed to enhance the diverse IoT-NR network performance while keeping the primary Wi-Fi network's performance to a bearable threshold. A Q-learning (QL) based algorithm is devised to maximize the normalized sum throughput of the coexistence Wi-Fi/IoT-NR network. The results confirm a maximum throughput gain of 51 % and ensure that the Wi-Fi network's performance remains intact. Finally, advanced levels of network security are critical to maintain due to severe signal attenuation at higher frequencies of 6G wireless communication. Thus, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices and ensure Quality-of-Service (QoS) for all the devices. In conclusion, the thesis has led towards efficient, secure, and smart communication and build foundation to address similar complex wireless networks.
... It achieves a good trade-off between efficiency and fairness. [9] concludes that the proportional fairness solution simultaneously achieves higher system throughput, better fairness and lower outage probability with respect to the default solution given by today's 802.11 commercial products [8]. [10] describes a model for elastic traffic in which a user chooses the charge per unit time that the user is willing to pay; thereafter the user's rate is determined by the network according to a proportional fairness criterion applied to the rate per unit charge. ...
This document is a study of fairness, feedback and stability notions of different packet transmission algorithms. We start the discussion with defining two scalable control algorithms namely primal and dual algorithm. We discuss the dual algorithm model and then understand the fair dual algorithm. Further, we discuss different notions of fairness under fair dual algorithm those correspond to TCP and RCP congestion control protocols. Feedback parameters are analyzed in each of these fairness algorithms and thus their stability is studied.
... Since the signal strength fluctuates independently for different stations, this strategy effectively exploits multi-user diversity. This is achieved by maximizing the sum of logarithmic throughput cost function of the VOLUME 9, 2021 individual stations [51]- [53], i.e., L =1 logR , whereR is defined in (9). ...
Long Term Evolution (LTE)-License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network, is a promising technique to address the spectrum scarcity issue in present and future wireless networks. However, unlicensed spectrum sharing between Wi-Fi and LAA requires fair resource allocation with specific performance guarantees for both sets of Wi-Fi and LAA stations. In this paper, an optimal communication policy is devised for LAA stations coexisting on a single unlicensed channel with Wi-Fi stations. The inter-network collisions are avoided through non-overlapping transmission phases for Wi-Fi and LAA networks. The throughput performance of LAA network is maximized while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The proposed scheme, unlike the state-of-the-art coexisting mechanism, jointly optimizes the transmission probability and the transmission rate for each LAA station. The formulated optimization problem to maximize network throughput is solved analytically. The numerical results demonstrate a significant improvement in the LAA throughput, more than 75%, as compared to the case when transmission probabilities are not optimized. Moreover, a notable gain of 8-9% in the fairness index reflects the intra-network fairness of the proposed LAA network over the conventional LAA network.
... We assume that the instantaneous channel gain for MTC device is perfectly estimated by the HAP, which is inline with the relevant prior works [37,66,67,99]. The details of the channel estimation process is outside the scope of this work. ...
... where ς is the scale parameter for the pdf . We assume that the instantaneous channel gain for each device is perfectly estimated by the BS, which is inline with the relevant prior works [37,66,67,99]. Note that the energy portion split by the devices for pilot signal transmission does not affect the design of the multi-user sequencing and scheduling. ...
... Since the signal power attenuation fluctuates independently for different devices, this strategy effectively exploits multi-user diversity. This can be achieved by minimizing the sum of logarithmic energy cost function of the individual devices [67,99,104], i.e., N i=1 log(E i ), where E i is defined in (4.14). For the proportionally-fair energy minimization objective, (4.15) becomes: ...
This thesis focuses on the modelling, analysis and design of novel communication strategies for wireless machine-type communication (MTC) systems to realize the notion of Internet of things (IoT). We consider sensor based MTC devices which acquire physical information from the environment and transmit it to a base station (BS) while satisfying application specific quality-of-service (QoS) requirements. Due to the wireless and unattended operation, these MTC devices are mostly battery-operated and are severely energy-constrained. In addition, MTC systems require low-latency, perpetual operation, massive-access, etc. Motivated by these critical requirements, this thesis proposes optimal data communication policies for four different network scenarios. In the first two scenarios, each MTC device transmits data on a dedicated orthogonal channel and either (i) possess an initially fully charged battery of finite capacity, or (ii) possess the ability to harvest energy and store it in a battery of finite capacity. In the other two scenarios, all MTC devices share a single channel and are either (iii) allocated individual non-overlapping transmission times, or (iv) randomly transmit data on predefined time slots. The proposed novel techniques and insights gained from this thesis aim to better utilize the limited energy resources of machine-type devices in order to effectively serve the future wireless networks. Firstly, we consider a sensor based MTC device communicates with a BS, and devise optimal data compression and transmission policies with an objective to prolong the device-lifetime. We formulate joint optimization problems aiming to maximize the device-lifetime whilst satisfying the delay and bit-error-rate constraints. Our results show significant improvement in device-lifetime. Importantly, the gain is most profound in the low latency regime. Secondly, we consider a sensor based MTC device that is served by a hybrid BS which wirelessly transfers power to the device and receives data transmission from the device. The MTC device employs data compression in order to reduce the energy cost of data transmission. Thus, we propose to jointly optimize the harvesting-time, compression and transmission design, to minimize the energy cost of the system under given delay constraint. The proposed scheme reduces energy consumption up to 19% when data compression is employed. Thirdly, we consider multiple MTC devices transmit data to a BS following the time division multiple access (TDMA). Conventionally, the energy-efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission time allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., does not have any impact on the energy-efficiency. We consider that data compression is performed before transmission. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate. Our results show that multi-user sequence optimization achieves up to 45% improvement in the energy-efficiency at MTC devices. Lastly, we consider contention resolution diversity slotted ALOHA (CRDSA) with transmit power diversity where each packet copy from a device is transmitted at a randomly selected power level. It results in inter-slot received power diversity, which is exploited by employing a signal-to-interference-plus-noise ratio based successive interference cancellation (SIC) receiver. We propose a message passing algorithm to model the SIC decoding and formulate an optimization problem to determine the optimal transmit power distribution subject to energy constraints. We show that the proposed strategy provides up to 88% system load performance improvement for massive-MTC systems.
... where ς represents the scale parameter for the probability distribution function. We assume that the instantaneous channel gain for each device is perfectly estimated by the BS, which is a reasonable assumption when the BS has no constraint on energy and data processing capability [6][7][8], [25], [26]. ...
... Since, the signal power attenuation fluctuates independently for different devices, this strategy effectively exploits multi-user diversity. This can be achieved by minimizing the sum of logarithmic energy cost function of the individual devices [8], [25], [31], ...
... respectively. An additional change of variable V n = ln D cp,n is necessary to transform the original problem for the proportionally-fair objective into the equivalent convex optimization problem (25). ...
In this paper, we propose joint sequencing and scheduling optimization for uplink machine-type communication (MTC). We consider multiple energy-constrained MTC devices that transmit data to a base station following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission block length allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., has not been considered as it does not have any impact on the energy efficiency. In this work, we consider that data compression is performed before transmission and show that the multi-user sequencing is indeed important. We apply three popular energy-minimization system objectives, which differ in terms of the overall system performance and fairness among the devices. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate control. Our results show that multi-user sequence optimization significantly improves the energy efficiency performance of the system. Notably, it makes the TDMA-based multi-user transmissions more likely to be feasible in the lower latency regime, and the performance gain is larger when the delay bound is stringent.
... It is shown that proportional fairness leads to an airtime-fairness, where equal airtime usage is provided to all STAs. Moreover, in a multi-AP WLAN, [28], [29] study AP-STA association problem with an objective to maximize the proportional fairness. More precisely, association control is implemented in the form of airtime allocation, where the transmission time of STAs at different APs are jointly optimized [28], [29]. ...
... Moreover, in a multi-AP WLAN, [28], [29] study AP-STA association problem with an objective to maximize the proportional fairness. More precisely, association control is implemented in the form of airtime allocation, where the transmission time of STAs at different APs are jointly optimized [28], [29]. ...
To manage and enable service customization among multiple internet service providers (ISPs) sharing the common physical infrastructure and network capacity in virtualized WiFi networks, this paper models and optimizes access point (AP)- station (STA) association via airtime usage control. More specifically, an optimization problem is formulated on the STAs’ transmission probabilities to maximize the overall network throughput, while providing airtime usage guarantees for the ISPs. As the proposed optimization problem is inherently non-convex, an algorithm to reach the optimal solution is developed by applying monomial approximation and geometric programming iteratively. Based on the proposed three-dimensional Markov-chain model of the enhanced distributed channel access (EDCA) protocol, the detailed implementation of the optimal transmission probability of each STA is also discussed by manipulating medium access control (MAC) parameters. The performance of the developed association and airtime control scheme is evaluated through numerical results. For both homogeneous and non-homogeneous STA distributions, numerical results reveal performance gains of the proposed algorithm in improving the throughput and keeping airtime usage guarantees.
... It groups mobile stations in order to improve total throughput by reducing the overall network overhead. A scheduling scheme was introduced in [27] for multi-rate multi-channel wireless networking, which employs a proportionally fair utility function. A similar technique for OFDMA (Orthogonal Frequency Devision Multiple Access) networks was proposed in [28], exhibiting a good trade-off between fairness and network throughput. ...
The 5G vision is not restricted solely to the wireless domain and its challenging requirements cannot be fulfilled without the efficient integration of cutting-edge technologies in all portions of the telecommunications infrastructure. The promoted architectures for next generation telecommunications systems involve high capacity network domains, which operate flexibly and seamlessly to offer full Quality of Experience to all types of subscribers. The proliferation of highly demanding multimedia services and the advanced features of modern communication devices necessitate the development of end-to-end schemes which can efficiently distribute large amount of network resources anywhere and whenever needed. The paper introduces a new resource allocation scheme for cutting-edge Fiber-Wireless networks is introduced that can be applied in the fronthaul portion of 5G-enabled architectures. The adopted technologies are the forthcoming 25G-EPON for the optical domain and the 5G-ready LTE-Advanced Pro for the wireless domain. The proposed scheme performs allocation decisions based on the outcome of an adjustable multi-stage optimization problem. The optimization factors are directly related to the major considerations in bandwidth distribution, namely priority-based traffic differentiation, power awareness, and fairness provision. The conducted evaluations prove that this approach is able to ensure high efficiency in network operations.
... In this article, we champion the BCO framework as a natural remedy for these practical challenges: as BCO readily addresses the issue of partial feedback from the environment, it obviates the need to infer network conditions. As an example consider traditional problem formulations concerning proportional fairness in WiFi networks (such as those in [8,9,10,11]). In these examples implementing the optimal solution requires retrieving network measurements and computing the channel access parameters that equalise the channel airtimes of the WiFi stations. ...
Bandit Convex Optimisation (BCO) is a powerful framework for sequential decision-making in non-stationary and partially observable environments. In a BCO problem, a decision-maker sequentially picks actions to minimize the cumulative cost associated with these decisions, all while receiving partial feedback about the state of the environment. This formulation is a very natural fit for wireless-network optimisation problems and has great application potential since: i) instead of assuming full observability of the network state, it only requires the metric to optimise as input, and ii) it provides strong performance guarantees while making only minimal assumptions about the network dynamics. Despite these advantages, BCO has not yet been explored in the context of wireless-network optimisation. In this paper, we make the first steps to demonstrate the potential of BCO techniques by formulating an unlicensed LTE/WiFi fair coexistence use case in the framework, and providing experimental results in a simulated environment. On the algorithmic front, we propose a simple and natural sequential multi-point BCO algorithm amenable to wireless networking optimisation, and provide its theoretical analysis. We expect the contributions of this paper to pave the way to further research on the application of online convex methods in the bandit setting.
... Traditionally, fairness in cellular networks is realized by dynamically allocating time, frequency [5], [6], or power [7], [8] to users according to strategies such as max-min or proportional fairness [9], [10], [11]. However, such a dynamic allocation can only be achieved with detailed channel state information at the transmitter (CSIT) from the whole network. ...
Increasing the density of base stations deployment is regarded as a means to satisfy the growing demand of wireless connectivity over a shared bandwidth, however increasing the disparity in the service received by each user. Fairness can be realized by dynamically allocating resources to users using detailed channel state information at the transmitter (CSIT), which constitutes an expensive overhead, especially for dense networks. One solution to improve the fairness without CSIT is to introduce spatial diversity through the use of relays in combination with physical-layer network coding (PNC). Most relaying-PNC solutions focus on the particular case of the twoway relay channel or they require additional relays and a large transmission time. In this paper we propose a multiple-relay communication protocol (MRCP) for achieving fairness in dense networks. It exploits spatial diversity without requiring additional relays since it uses the base stations as relays. Furthermore, MRCP is applicable to an arbitrary number of base stations and users, while keeping a small transmission time. We show that our approach achieves the highest max-min fairness among users and almost full diversity with asymmetric transmissions.
... The work presented in [46] corresponds to the first part of a series of results regarding to the study about the use of the Proportional algorithm usefulness function as a basis for resources assignment and wireless networks planning of multiple multichannel speeds. They present the fundamental properties and the physicaleconomic interpretation of the Proportional optimization. ...
This work presents a multivariable adaptive spectrum handoff model for cognitiveradio networks that allows improving secondary user's spectrum mobility from theintelligent selection of spectrum opportunities called MAPMFF. Four algorithmsfor decision making during a spectrum handoff, were developed with different
approaches, fuzzy, feedback statistics, predictive and multichannel, which make up
the MAPMFF proposed model. The best spectrum opportunities were selected
dynamically based on the following decision criteria, the probability of channel
availability, estimated time of channel availability, Signal-to-interference-plusnoise
ratio, and bandwidth, which were determined through the modified Delphi
method and corresponding weights were calculated through the developed FFAHP
algorithm. In order to assess the performance of the developed algorithms, a
comparative analysis was made between these algorithms and the most relevant
spectrum handoff algorithms in the current literature. Unlike other related papers,
benchmarking was validated through a trace of real spectrum occupation data
captured in the frequency GSM band, which characterizes the real behavior of
primary users. In the validation phase, four assessment scenarios were proposed to
40 Cesar Hernández et al.
consider, two kind of applications, real-time and best-effort; two traffic case
scenarios, high and low and, ten evaluation metrics, number of handoff, number of
failed handoff, bandwidth, delay, throughput, fairness, number of handoff without
interference, number of handoff with interference, number of perfect handoff andnumber of anticipated handoff.
The main contribution of this work is the development of an adaptive spectral model
(MAPMFF) with excellent performance according to the results of validation with
real occupation spectral data in 4 different scenarios, with four types of traffic, and
under ten evaluation metrics. Consequently, the impact reached by the MAPMFF
model is the improvement in the quality of service parameters for mobile
communications.
The proposed multi-model selects the best spectrum handoff algorithm, among the
four models developed in this work. This is according to the spectrum
characteristics and requirements of the secondary user, providing an efficient andeffective process of frequency channel selection. Then, the MAPMFF model
behaves as an expert system when inferring from data known from a wireless
application and spectral occupation the best spectral handoff algorithm. This is inorder to analyze and decide which spectral opportunity is the appropriated to theuser requirements by an inference engine.
The results of the comparative analysis with other spectrum handoff algorithmsshow that the MAPMFF model provides the best performance in the ten evaluationmetrics for the four proposed scenarios. According to the obtained results, theproposed model has a low average handoff rate, a high percentage of averagethroughput and average bandwidth, and a low level of average delay.
