Adaptive Power Control in Peer to Peer Networks
Hani Abdelwhahab SaedAhmed Mohammed
Thesis submitted for the degree of
Master in Electronics and Computer technology 60 credits
Department of Physics
Faculty of mathematics and natural sciences
UNIVERSITY OF OSLO
Adaptive Power Control in Peer to Peer Networks
Hani Abdelwhahab SaedAhmed Mohammed
© 2019 Hani Abdelwhahab SaedAhmed Mohammed
Adaptive Power Control in Peer to Peer Networks
Abstract
WLAN (Wireless local area network ), also known as Wi-Fi has gained lots of attention during the past two decades due to its promising capabilities, ease of installation and low-cost of access points (APs), which offers substantial economic benefits. WLAN is a dynamic, flexible replacement of wired local area networks. It is already implemented in most of the devices nowadays. For instance, laptops, smartphones, smart TV’s, computers, and the internet of things devices(IOT). Moreover, WLAN is the primary connection method to the internet for more than 76% of North-American households.
Although power control algorithms have been considerably studied in the research community, their practical implementation is still primitive. In general, such improvements urge to profound changes in current standards. Toward a solution to succeed, it is necessary to be efficiently implementable over actual hardware and software systems. In this aim, this thesis studies whether new power control methods can be implemented using today’s systems and off-the-shelf wireless cards to enhance spatial reuse, link throughput and reduce packages errors. Through the evaluation of several Atheros cards, we address some issues related to the implementation of power control to accomplish better link quality. Our observations show that several existing wireless Routers do cause more harm to the radio environment due to poor network design.
Dedication
To the martyrs of Military headquarter in Khartoum, To the brave men and women fighting for democratic change in Sudan.
To my Mom, who made me the way I am today.
To my lovely wife Sara.
To my beautiful two angles Talia and Nagi.
Acknowledgements
I would like to take this opportunity to thanks my supervisor Professor Torleiv Maseng, for believing in me and giving me the chance to work with a living legend. I am greatly in debt to Madeleine Rønning for her help and guidance as i would like to thank Terje Mikal Mjelde from FFI for his help.
Contents
1 Introduction 9
1.1 Introduction . . . 9
1.2 Structure of the Thesis . . . 11
2 Background and related work 12 2.1 IEEE802.11 Standards: . . . 13
2.1.1 IEEE802.11a . . . 13
2.1.2 IEEE802.11b . . . 13
2.1.3 IEEE802.11g . . . 13
2.1.4 IEEE802.11n . . . 14
2.1.5 IEEE802.11ac . . . 14
2.2 Interference . . . 15
2.2.1 Interference types: . . . 16
2.2.2 Inter-symbol interference (ISI) . . . 16
2.2.3 Adjacent Channel Interference (ACI) . . . 16
2.2.4 Co-channel interference . . . 17
2.3 Hidden Node Problem . . . 17
2.4 Related work . . . 19
3 Theory and TPC algorithm 21
4 Methods and achievements 25
4.1 Test-bed layout . . . 25
4.1.1 Coordinator . . . 26
4.1.2 Access Points (APs) . . . 26
4.1.3 Clients . . . 28
4.2 Software . . . 28
4.2.1 Openwrt . . . 28
4.2.2 Iperf . . . 28
4.2.3 IW . . . 29
4.2.4 ZMQ . . . 29
5 Results and Discussion 32 5.1 Results . . . 32
5.1.1 RX . . . 33
5.1.2 Link Throughput . . . 35
5.1.3 Retries . . . 36
5.2 Summary . . . 37
6 Conclusion 41 6.1 Conclusion . . . 41
6.2 Future Work . . . 41
List of Figures
2.1 The 2.4 GHz frequency spectrum . . . 14
2.2 The 5 GHz frequency spectrum[1] . . . 15
2.3 Scenarios 1, 2, 3 . . . 17
2.4 Scenarios 4, 5, 6 . . . 18
3.1 TPC Algorithm . . . 24
4.1 Testbed Building . . . 27
4.2 Producer-Consumer model . . . 30
5.1 Devices . . . 32
5.2 Recieved power AP2, AP4 . . . 34
5.3 TCP and Physical throughput AP2 . . . 38
5.4 TCP and Physical throughput AP4 . . . 39
5.5 Re-transmission AP4 . . . 40
List of Tables
4.1 The test-bed configuration. . . 31 5.1 The test results. . . 35
Abbreviations
TX Transmit power
RRM Radio Resource Management SIR Signal to Interference Ratio
SINR Signal to Interference and Noise Raio AP Access point
Chapter 1 Introduction
1.1 Introduction
WLAN (Wireless local area network ), also known as Wi-Fi has gained lots of attention during the past two decades due to its promising capabilities, ease of installation and low-cost of access points (APs), which offers substantial economic benefits. WLAN is a dynamic, flexible replacement of wired local area networks. It is already implemented in most of the devices nowadays. For instance, laptops, smartphones, smart TV’s, computers, and the internet of things devices(IOT). Moreover, WLAN is the primary connection method to the internet for more than 76% of the North-American households[2].
With the increase of Wi-Fi network deployment in houses, offices, airports, and malls in unplanned, chaotic manners, appears the need for Radio resource management(RRM) is raised. Transmission power control (TPC) is one the RRM interference mitigation techniques, and it is essential to overcome 2.4GHz impairments caused by poor spectral efficiency. TPC helps in interference minimisation, power consumption reduction, range control, and link robustness.The mitigation of Co-Channel interference in dense WLAN environments is of extreme importance in today’s ICT infrastructure. The typical imple- mentation of the default transmission power of APs and their clients is usually set o the maximum level of 20 dBm without careful deliberation of the channel state. Doing so is causing more harm than intended, since it leads to increase co-channel interference and
might also result in a hidden node problem due to link asymmetry[3].
The Institute of Electrical and Electronic Engineers(IEEE) Started standardising the wireless connectivity of WLANs, and their clients in 1997, under the standard IEEE 802.11, WLANs operate in the unlicensed ISM (The industrial, scientific, and medical) frequency. International Telecommunication Union (ITU) restricts the WLANs in two bands(5GHz, and 2.4GHz).
Although power control algorithms have been considerably studied in the research community, their practical implementation is still primitive. In general, such improvements urge to profound changes in current standards. Toward a solution to succeed, it is necessary to be efficiently implementable over actual hardware and software systems. In this aim, this thesis studies whether new power control methods can be implemented using today’s systems and off-the-shelf wireless cards to enhance spatial reuse, link throughput and reduce packages errors. Through the evaluation of several Atheros cards, we address some issues related to the implementation of power control to accomplish better link quality. Our observations show that several existing wireless Routers do cause more harm to the radio environment due to the poor network design.
The mitigation of Co-Channel interference in dense WLAN environments is of major importance in today’s ICT infrastructure. The growth of the numbers of devices that communicate via radio waves. i.e via wireless communication.A node is only to be associated and communicates with its adjacent AP. Reducing the transmit power of the AP to a certain level that still guarantees reliable communication between AP and clients, the interference to other AP in the nearness could be minimized Since the signal strength diminishes with distance. Furthermore, other APs and its associated clients at a certain distance can use the same channel with no interference. This method allows many networks to operate at the same time in a given region while using only a limited number of wireless channels.
1.2 Structure of the Thesis
In this thesis, Chapter 2 serves as an introduction to the different WLAN specific standards, types of interference, and some of the interference mitigation techniques. Additionally, some related work will be introduced and discussed, the approaches, their pros and cons.
Chapter 3 analysing the theory behind the downlink transmit power control, formulate a consistent model to tackle the obstacle of the Co-Channel interference. Further, we will outline our TPC. Chapter 3 presents the TPC algorithm, its implementation, the test-bed layout, the hardware, and software used in the test-bed, and the nodes configurations.
Within chapter 5, the result of our experiment will be revealed and discussed. Chapter 6 concludes the finding of our work, and future work recommendation will be presented.
Chapter 2
Background and related work
Lately, wireless cards are implemented in most of the modern devices that share the ISM band. The International telecommunication union (ITU) had limited this band for the Industrial, scientific and medical application and purposes. Nevertheless, over the years, the use of the ISM band has grown among Wi-Fi and similar low-power short-range Communication applications. In addition to some of the non-communicating technologies such as microwave ovens, and lots of communicating systems such as gaming controllers, Bluetooth headphones, smart TV’s, IOT devices, and similar devices. Certain technologies share the Wi-Fi radio band, which leads to degradation in their services efficiency due to the increment of interference in this frequency band. The amount of the interference is determent by whether a client is close or far to the AP to the interfering sources[4].
In [5], the authors affirmed that the TCP-dominant Workload, cumulative throughput is characterised by the number of interfering APs rather than the number of active clients in the network.
2.1 IEEE802.11 Standards:
2.1.1 IEEE802.11a
On September 1999 ieee802.11a standard was published. The specification of this standard explicated that it operates in two frequency bands 5 GHz, and 3.7 GHz. The modulation technique used in ieee802.11a is orthogonal frequency division multiplexing(OFDM) with a bandwidth of 20 MHz. Networks implemented under this amendment support a data rate of 54MHz, which is higher than the data rate of ieee802.11b standard. Routers that adopt this standard implements single input single output antenna(SISO), and the overall performance of their networks showed much lower Interference particularly in the 5GHz in comparison with the networks that use ieee802.11b due to the higher number of non-overlapping channels. See channels distribution in Fig 2.2 [6, 7, 8]
2.1.2 IEEE802.11b
In correspondence with ieee208.11a, the standard ieee208.11b was issued. Networks utilise this standard operate on the 2.4GHz frequency band and a bandwidth of 22MHz.
IEEE802.11b supports 11Mbps, and can also drop back to 5.5,2 and 1 Mbps. Routers use this standard use Direct Sequence Spread Spectrum (DSSS) with only SISO antenna. This standard is subject to the interference as a consequence of the high density of deployment in a frequency band with only three non-overlapping channels. Channel distribution is shown in see Fig (2.1) [6, 7, 8]
2.1.3 IEEE802.11g
The IEEE802.11g was delivered in 2003. It supports a maximum bandwidth of 54Mbps.
This standard is a revision of IEEE802.11b. It operates in the band of 2.4 MHZ. The standard of 802.11g supports transmission bandwidth of 20 MHZ. IEEE802.11g adopts a combination of OFDM and DSSS, with support of SISO. Networks that use IEEE802.11g perform much better than 802.11b in regards to the data rate.
Figure 2.1: The 2.4 GHz frequency spectrum [1]
2.1.4 IEEE802.11n
In an enhanced version of the former Standard, IEEE802.11n was released in 2009. Anew technology was utilised in this standard which is Multiple Input Multiple Output (MIMO), this technology supports the use of multiple antennas in the transmitters and receivers to profit from the simultaneous transmission of the radio waves. MIMO enhances network throughput and transmission range. Furthermore, IEEE802.11n provides two bandwidth bands 20, 40 MHz, The beamforming which is the use of signal processing techniques to send radio beams in precise directions, and OFDM modulation scheme which improves the theoretical throughput to 300 MHZ in the 5Ghz band. For channel distribution see Fig 2.2 and (2.1(Some Routers supports dual-band configuration which is totally depending on the capability of the wireless card implemented in a particular router.[6, 7, 8]
2.1.5 IEEE802.11ac
IEEE802.11 was published in December 2013. It supports dual mode(operating in both 2.4 GHzand5 GHzsimultaneously) , several transmission bands (20,40,80, and160 MHz, beamforming and Multi-user MIMO. A wider channel and MU-MIMO helps IEEE802.11g
Figure 2.2: The 5 GHz frequency spectrum[1]
Some other amendments [9]:
• IEEE802.11k Radio Resource Managements of wireless LAN.
• IEEE802.11rFast Basic Service Set(BSS) Transition.
• IEEE802.11wProtected Management frames.
• IEEE802.11pWireless Access in Vehicular Environment.
• IEEE802.11adEnhancements for Very High Throughput in the60 GHzBand.
• IEEE802.11af Television White Spaces (TVWS) Operation.
2.2 Interference
One of the physical phenomenons in radio waves propagation behaviour is interference.
It occurs due to the superposition and the characteristics of the wave propagation through spectrum medium shared among multiple stations. Furthermore, In most of WLAN, networks interference is not coordinated, which limits the network capability to accomplish its highest performance by degrading network throughput[10]. 802.11 networks employ carrier sense multiple access collision avoidance CSMA/CA to regulate the communication between nodes and APs. Nodes sense the channel before transmitting a frame; if the
channel is accessible, they send the frame and release the channel when they are done.
Differently, they wait until the channel is freed. The sensing mechanism may cause the hidden node problem if two nodes can communicate with the access point, although they cannot sense each other. In this circumstance the node may assume the availability of the channel, and start transmitting its data, Nonetheless the node information, control messages may collide with other transmissions in the radio channel.
2.2.1 Interference types:
There are various types of radio wave interference; the crucial types are: Interference from APs operate in the same channel, known as co-channel interference(CCI), Interference due to nearby APs that operate in an overlapping channel which is known as adjacent-channel, and an interference that causes the data symbols changing its characteristics as a result of the multiple paths the signal takes, which know as Inter-symbol interference (ISI.
2.2.2 Inter-symbol interference (ISI)
Inter-symbol interference (ISI) arises due to the limitation of the transmission channel and the filtering at the transmitter, receiver or the channel. As a signal waveform pulse transmitted over a band-limited channel if the signal pulse bandwidth is equal or slightly less than the channel bandwidth, the spreading of the pulse will spread its symbol duration and induce the pulse to smear over the channel bandwidth and cause signal pulses to interfere [11]. ISI causes a higher error rate and increasing the transmit power grant no improvement to this problem. Nevertheless, pulse shaping is one of the technique to reduce or eliminate ISI.
2.2.3 Adjacent Channel Interference (ACI)
concern[11, 12]. It is more severe than the (CCI) and may cause more degradation in link quality.
2.2.4 Co-channel interference
Co-channel interference (CCI) is defined as the undesired communication operates into the same channel of interest. The issue of spectrum management becomes a significant challenge, mainly for IEEE 802.11 b and g standards considering there are only three non-overlapping channels. While the situation is much better for IEEE 802.11 a and ac.
Interference causes by coexisted neighbour APs, using the same channel results in critical degradation of WLAN throughput.
2.3 Hidden Node Problem
TPC seeks to minimize the transmit power to the lowest level with which AP grantees a reliable communication link with its clients. As a result of reducing TX power in a dis- tributed fashion, some clients may suffer from link asymmetry and hidden node problem.
Since WLAN networks employ the sensing mechanism in CSMA/CA, so it is required for a client to be able to sense other clients in the network Further, Implementing TPC can
(i) 1 (ii) 2 (iii) 3
Figure 2.3: Scenarios 1, 2, 3 lead to one of the five different scenarios :
(1) TPC results a complete spatial reuse when in both networks are totally separated, and they are not in each other transmission range, which is the stablest scenario. See figure (2.3i)
(2) TPC results in complete spatial reuse when APs (Transmitters in this case) can sense each other, while regarding clients (receivers in this case) the sensing is not mutual. In other words, receivers in both networks are not able to sense each other. In this situation, TPC provides no improvement in network throughput due to the increase in packages errors and package re-transmission. See figure (2.3ii)
(3) This situation is known as the link starvation problem. It is a consequence of the channel asymmetry in the link between TX1 and RX1. see Fig2.3iii TX2 can not sense TX1 transmission, and TX2 always assume a clear air which results in a collision between packages.
(i) 4 (ii) 5 (iii) 6
Figure 2.4: Scenarios 4, 5, 6
(4) TPC in 2.4i cause packages re-transmission due to the channel asymmetry as a result of simultaneous transmission.
(5) TPC in 2.4ii, and 2.4iii shows the typical hidden problem where receivers can sens both APs despite that APs are not in their transmission range.
2.4 Related work
The Transmit power control has been a topic of research for at least the past two decades.
In particular, there is major work in the areas of wireless local area networks spatial reuse improvement. However, only a few of these works consider the starvation problem.
Among those research, there are three popular method to estimate channel status: (1) frame loss monitoring (2) received signal strength monitoring , or a combination of both (1)(2).
in [13]The authors suggested TPC based on Signal to noise interference (SNR) scheme.
In their work, Sheth and Han exhibited some of the practical issues such as the layer should the power control be implemented in , when TPC should be updated, and how to account for mobility. They implemented a receiver-driven algorithm where the receiver performs most of the calculations and then the receiver suggests an optimal TX. Yet, unnecessary power consumption and overhead at the receiver is introduced an in practical option.
Also, their method in verifying the throughput measurement by using just using the UDP protocol, raise questions about the entire method, since UDP is assuming that the end-user receives all packages, which is not true since UDP is not strongly effected by latency and data rate.
With a combination in colouring and carrier sensing threshold, dynamic TX Power.
the work in [14] introduced an interference based dynamic channel access algorithm for Dense deployments WLAN. The work is simulated in Matlab and requires a change in the Clear Channel assessment threshold, which is hardly possible to practically implement i due to vendor related impairments. Another work that uses the CCA adjustments is in this paper[3], the authors proposed a cross-layered approach that jointly tuned the transmit power and carrier sensing threshold. They tackled the problem of the hidden node due to asymmetric links. The demonstrated the possibility of achieving asymmetric link while sending AP information to neighbouring APs via beacon frames. With a network free of hidden nodes problem. The authors concluded that is the transmit power is high, so the carrier sensing threshold (CST) has to be low. Notwithstanding the promising results, The
algorithm can be implemented in a simulation test-bed.
in [15] the authors, with the motive of providing better performance and coverage in a dense network, deployed a link layer and physical layer approach. Their solution includes transmitting power, data rate and carrier-sense threshold to tackle the problem of interference results of the shared nature of the propagation medium. In Ad-hoc networks, most of the power control, data rate and carrier sensing approaches could be accomplished because in ad-hoc networks, each node can communicate with any other node in the transmission range.
in [16] based of frame loss rate (FLR), The authors proposed an algorithm to mitigate RF interference and to eliminate the problem of link starvation by managing the data rate, transmit power and carrier sensing threshold. They claimed that by reducing the transmission power and the CTS the network can avoid asymmetrical links and facilitate more spatial reuse.Further, in [17] , the authors proposed TPC scheme in attempt to increase the area spectral efficiency in a interference limited channel. By using an indicator issued in neighbour AP where each AP reports its channel occupancy rate(COR) reporting from its associated clients, as an indicator can be used by other APs that hear the indicator.
Based on a statistical modelling of frame loss and the transmission opportunities. A novel algorithm is developed by Richart et al. [15] Based on a statistical modelling of frame loss and the transmission opportunities. A novel algorithm is developed byRichart et al. [15]
. In their work the authors , claimed that their solution adapts the transmit power while reducing the carrier sensing threshold simultaneously. their algorithm was tested in a simulator and outperformed three of the will know power control and data rate adaptation algorithm. However as it was mentioned before, implementing CCA is not achievable in today’s hardware.
Chapter 3
Theory and TPC algorithm
3.1 Downlink Power control algorithm
The transmitted power of of an access point is the radiated intensity transmitted from AP antenna in all directions. this amount of power attenuated by a certain ratio depends on distance between the transmitter and the receiver. Radio link performance is determined by the ratio of its received signal power by the noise plus interference 3.1 .
SI N R= Pr
N+ I (3.1)
All the equations are in dBm unless specified otherwise
In equation (3.1): Pr : is The received power at the receiver antenna.
N : is The thermal noise power I : is the interference power.
Where asP,I,Ndepend on the receiver filters.
To grantee a functional link between an AP and its associated clients, the Received power at the client received antenna should be higher than the receiver sensitivity. by ignoring the noise in equation 3.1, and assume its a white noise, and since the power is
often measured in dB
20 log10(SI R(i))=20 log10(Pr(i) −I) (3.2) WhereSI R is the signal to interference ratio of a clienti, Pr(i)is the received power at of the clienti . Radio channel tends to be unstable, and the measured values can vary.
Therefore a margin variable is introduced to equation 3.1.
SI R= Pr(i) −maxIr(i) − M(i) (3.3)
Pr(i) ≥maxIr(i)+ M(i) (3.4)
Where M(i) : is link margin. The condition in equation 3.1 is essential to all the associated clients. Since Interference power at each associated client varies, and to ensure that the power reduction would not lead to poor link quality at the worst client and thus causes the hidden node problem, The importance of this condition that it grantees the best link quality at a specificTX.
3.2 TCP algorithm
Down link is the direction of transmission from the access point to a client associated with the AP. TCP decides the optimal transmit power by improving the worst link in the network, that suffers from CCI. AP sends a TPC request to its associated clients(STAs) to measure their received power, scan the the all neighbouring APs in all the channels. Each STA transmits with its maximum power (20 dBm). When a STA received a TPC request,
table for the next measurements. Each STA path loss and link margin are calculated at the AP.
Based on the received information from all the associated clients, AP finds the worst link based on its received power, the worst link based on RX and the received power of the maximum interference nearby STA. The value of 5 dBM is chosen as a margin lower than the measured RX. Next, TPC calculates the Signal to Interference and Noise Ratio (SINR) from equation 3.1. Furthermore AP tests the value of SINR of the worst link with at least one AP of its neighbouring APs, which operates in an overlapping channel.(i.e Any AP that operates on channel 1,2,3,4,and 5). If the value of SINR is a positive value, so the current power shall be increased by a value equals to the SINR. Otherwise, the power shall be reduced by the value of SINR. Further, the new TX should not cause one of the clients to fall below the interference threshold, so TPC will predict whether the new TX will cause RX at the received to fall below the threshold on not, see Fig 3.1
Figure 3.1: TPC Algorithm
Chapter 4
Methods and achievements
This chapter covers the implementation of the experiment, the code, and how the code works. Different packages and software were utilised in the test-bed. The test was im- plemented at the Norwegian defence research establishments (FFI). FFI provided devices and equipment, and the majority of the work is performed via remote access from Kjeller campus (University of Oslo).
The building at the Norwegian defence establishment simulates real-life block-residential building, where it is four-floors building with varying number of wireless networks in the neighbouring area. Multiple of these networks operate in the same radio channel 4.1 The purpose of our literature studies was to formulate, understand and find a suitable approach to undertake the Co-channel interference problem. An algorithm has developed a prototype in Python was build in both APs and clients. The underlying assumption was that this algorithm would improve network throughput, especially in the area with the highest co-channel interference.
4.1 Test-bed layout
The test-bed consists of four nodes installed in the ceiling of the first floor of the building, illustrated in Fig 4.1. This effort is to simulate an urban residential building. Nodes were connected to a coordinator. Furthermore, the coordinator controls the connectivity to the
internet and secures the remote access to the other nodes. The coordinator node provides and creates a Secure Shell (SSH) sessions to execute commands and perform tasks. All the nodes, as well as the coordinator, run a version of Linux with a command line interface.
Nodes are small computers equipped with identical hardware listed below. Access points and clients software will be mentioned later in this chapter.
The test-bed consists of four nodes installed in the ceiling of the first floor of the building, illustrated in Figure (5.1). This effort is to simulate an urban residential build- ing. Nodes were connected to a coordinator. Furthermore, the coordinator controls the connectivity to the internet and secures the remote access to the other nodes. The co- ordinator node provides and creates a Secure Shell (SSH) sessions to execute commands and perform tasks. All the nodes, as well as the coordinator, run a version of Linux with a command line interface. Nodes are small computers equipped with identical hardware listed below. Access points and clients software will be mentioned later in this chapter.
Soekris net6501-01 computers
Atheros Wireless Card AR9280 Mini PCIe
Power Supply, 12V,30A, IEC320-C8 inlet 90V-264V
Tri Band Rubber Duck RP-TNC antenna 2.4 /4.9/ 5.8 GHz Intel mini PCIe SSD Drive 525 30GB MLC OEM
4.1.1 Coordinator
Coordinator is the a central computer that controls the accessibility of users to the test- bed through command-line interface(CLI). It does not control any of the measurements operations or the execution of the power assignment that are done by the APs.
Figure 4.1: Testbed Building
geographical area. There are two nodes run OpenWrt 18.06 and function as access points.
Each AP contains all the software and scripts which are needed to perform all the tasks of an AP. Each of our APs creates an independent WLAN separated from the other WLANs regarding wireless connectivity, configured as a standalone network.
An access point determines its optimal transmitting power based on the reported measurement from the associated clients. These measurements include received power at every associated client along with the received power of other access points affecting a specific client. An AP sends aTPCrequest to each one of its associated clients, the client measures the received power, scans the surrounding environments for other access points and report back to the access point. The messaging protocol between AP and its clients is done by ZMQ see 4.2.4. The data rate is set to the maximum that 802.11n offersMCS15.
It is set to the highest to assure that the airtime that the AP could interfere with others could be the minimum. The data rate then would be maintained by the default minstrel algorithm which is implemented inAtherosdrivers [18].
4.1.3 Clients
As it is shown in figure 4.1, A client is a small computer with a Debian Linux installed.
All the associated clients are identical in regards to software and hardware. A Python script runs in the background of the system to measure the received power of the client and to scan the radio environment and report back to the AP using ZMQ. See4.2.4 the pull-push model.
4.2 Software
4.2.1 Openwrt
OpenWrt is a highly adjustable operating system which is based on Linux/GNU[19]. It is easily extensible, can be modified and adjusted for most of the embedded systems such as routers.OpenWrt is a replacement of a vendor firmware which gives the ability to add more features and packages. It provides a fully writable file system with the capability of adding and managing packages.
4.2.2 Iperf
Iperf is a cross-platform software for actual measurements of the maximum obtainable bandwidth and link quality of on IP networks. It measures throughput, bit rate, package loss and some other parameters of the links between a server and client. It supports adjusting various parameters related to timing, buffers and protocols (TCP, UDP, SCTP with IPv4 and IPv6). For each test, it reports the bandwidth, loss, and other parameters.TCP test TCP test reports link throughput while UDP test reports package loss
Iperf is used in our test-bed as a TCP client server model , it is used to test the throughput of the link between APs and its associated clients.
The command in 4.2.2 runs the node as a server or data sink.
AP side
iperf -c $IP -y c -t 10 | cut -f9 -d ’,’
and the command4.2.2 runs the node as a client or data source, and retrieves the throughput measurements.
4.2.3 IW
IW is a new command-line interface (CLI) that replaces the previous popular Wireless extension in Linux based systems. This configuration tool that configures nl80211 supports all the new driver that has been added to Linux kernel. A distinguishing feature of the measurement retrieved from Atheros drivers is its accuracy since it is known that Athers drivers use a smoothing mechanism to average the values of RSSI and Power and link quality from the last received packages. For instance, assigning a particular Tx-power is done by using the command: The new TX-power is set by the command:
iw phy phy0 set txpower fixed $txpower$
Assigning the highest data rateIEEE802.11n supports with a modulation index of 15, all that is done by using the command :
iw dev wlan0 set bitrates ht-mcs-2.4 15
4.2.4 ZMQ
PYZMQ is the python of ZeroMQ(0MQ) which is a lightweight messaging product. it started in 2007 and since then it has been an open source project with more that 50 named contributors. 0MQ is so popular and has an active community that provides education and support.It is based on the client server architecture of Berkeley sockets API (BSD sockets). This API is easy to learn, and conceptually identical in-spite of the programming language in use.
Figure 4.2: Producer-Consumer model
In our test-bed, a messaging protocol is built between the associated stations as servers (Producer), and the APs as clients(result collectors). A request is issued from each access point towards its associated clients. In other words, an associated client pushes the required information (Received power(Rx) and the received power of the surrounding APs at the client antenna), when it gets a request from AP, then the access point pulls the result.
Table 4.1 shows the test-bed configuration and the fixed parameters in both access points and clients. Two campaigns are carried. In the beginning, we have tested transmit power,links throughput, retries, and received power at the client’s while AP transmitting with the maximum transmit power. Later, we have measured all the parameters mentioned above again while TPC was running. A comparison between the achieved result in both cases are shown and analysed in chapter 5.
APs and Clients Configuration
Type APs clients
Mode Master Managed
Frequency band 2.4 GHz 2.4 GHz
Channel 1 1
Channel band- width
20MHz 20 MHZ
Power Type TPC fixed at 20 dBm
Operating System OpenWrt Dubian Table 4.1: The test-bed configuration.
Chapter 5
Results and Discussion
5.1 Results
This chapter introduce the results of our experiments carried to demonstrate and verify the proposed TPC algorithm. As mentioned before, The TPC algorithm is implemented as an
Figure 5.1: Devices
or 5 GHz). The 2.4GHz (see ieee802.11g) was chosen as the primary standard for the test- bed due to the smaller number of independent channels. The Access point is configured in Master mode. Iperf 4.2.2 (version 2.0.12 (25 June 2018) Pthreads) was used to measure link throughput in both APs, and clients. APs were configured as Traffic sources (iperf clients) while clients were configured as traffic sinks (iperf servers). ZMQ 4.2.4 was used as a TCP messaging model to report Received power, and the channel state. All APs have been set to operate on the same frequency channel, which is channel one. Channel one has a center frequency of 2412, and all AP operates with 20MHZ bandwidth.
.
Figure 5.1 shows the test bed layout. The distance between the two APs (AP2 and AP4) is 5 meters. One associated client (STA1) was connected AP2 while STA2 is associated with the router AP4. The distance between STA1 and AP2 is 2meters while the distance between the same client and AP4 is 3 meters. So we expected a bad channel environment at client STA1 due to the small distance between the client, and an access point that operates is the same channel. STA2 was located 8 meters further away from AP4.
Two campaigns are carried. In the beginning, we have tested transmit power,links throughput, retries, and received power at the client’s while AP transmitting with the maximum transmit power. Later, we have measured all the parameters mentioned above again while TPC was running. A comparison between the achieved result in both cases are shown and analysed in chapter 5.
5.1.1 RX
Figures 5.2i, and 5.2ii show two plots each, the one on the top represents the received power at client STA1. This figure aims to compare the received power using the maximum transmit power to the received power when the TPC algorithm was used. The y-axis in the two plots represents the power measured in dBm, while the x-axis represents the TPC decisions interval. The fluctuation which appears in the two figures due to the change in transmit power, and the state of the channel, since the channel frequency response is not
(i) received power at AP2
fixed, also the method of smoothing the last measured Received power which is done at the driver level in Linux wireless driver, which is done by Minstrel [18], since Minstrel uses the exponential weighted moving average to process the serious of measurements.
As the figure is shown, the variation in the transmit power regarding AP2 tends to make more giant steps than in AP4. This because of the instability in the radio channel. Since STA1 is much closer to AP2, in addition to the rapid change in AP4 TX influences AP2 to change its TX accordingly. While in the case of AP4, it takes closer steps to compensate for the increase of AP2 transmit power.
Test Scenario
Network id AP2 AP4
No of clients 1 1
Physical 20dBm 50.814154 42.094207
Physical TPC 49.196165 43.530736
TCP 20dBm 31.860901 33.938699
TCP TPC 28.069515 39.614050
Retries 20dBm% 0.089477 0.012608
Retries TPC% 0.062091 0.012140
Table 5.1: The test results.
5.1.2 Link Throughput
AP2
In comparison with transmitting with the maximum power, STA1 achieved lower through- put when TPC is tested. The average link throughput obtained was 12% lower than the one accomplished when TX was 20 dBm. The reason for that STA1 has failed to achieve the same throughput, is because of (1)the client STA1 location is in the range of both access points transmission range. (2) The distance between STA1 and the interfering access point is only 3 meters far. See fig 5.1. AP2 throughput is illustrated in fig 5.4. Throughput was
measured using two different mechanisms (TCP using iperf, and physical using Minstrel).
TCP link throughput is plotted in Figure 5.3i. The fluctuation appears in the plot, is an indication of the channel instability due to several reasons such as Multi-path interference, shadowing or white noise, and similar. In this case, Minstrel [18], which is implemented in the wireless driver, chooses the suitable data rate based on retries chain and calculates link throughput accordingly. Link throughput is calculated based on equation 5.1. Another type of measurements is shown in figure 5.3ii. These measurements are done based on Minstrel algorithm. The result shows the same pattern of fluctuation due to the channel instability along with the rapid changing in AP4 TX power.
T hr oughput = Pr obsuccesstr ansmission×M egabitstr ansmitted
time f or1tryo f1packettobesentont heair (5.1) AP4
As can be seen in figure 5.4i, The link AP4 STA2 achieved 16% greater throughput in comparison with when AP2 transmitted with its maximum power, which is understandable since AP4 STA2 has better link quality, and the distance between STA2 and AP1( which is the interfering power) is 13m. Referring to the table 5.1, It can be concluded that STA2 has achieved better average throughput than STA1 in the two link throughput categories(TCP and Physical). Since STA2 measured an average TCP throughput of 39.6 MHz, and an average physical throughput of 43.530736 which is almost the same physical throughput registered at the maximum power.
5.1.3 Retries
As it is shown in figures 5.5 both networks have demonstrate an improvement in the proportions of packages re-transmission. STA2 in 5.5ii has shown smaller number of retries due to its link quality. and because of its location, and radio channel status around.
when TPC was running, the percentage of the packages kept decreasing as the experiment proceeds.
5.2 Summary
First, we have tested both networks with two access points were transmitting with their maximum power, which is 20 dBm. STA1 suffered a high percentage of packages re- transmission, which was expected due to its location and the surrounding environment around this client. Despite the well-received power(higher RSSI). See fig 5.2i The link between AP2 and STA1 had achieved 97% of its throughput when it was measured using the implemented TCP iperf.On the other hand Minstrel data rate algorithm measured 88%
as its physical throughput. The link is still reliable, the interference is not fully eliminated.
However, STA2 achieve higher throughput, better link quality.
(i) TCP Throughput at AP2
(i) TCP Throughput at AP4
(ii) Physical Throughput at AP4 39
(i) Re-transmission AP2
Chapter 6 Conclusion
6.1 Conclusion
Prior work has documented the importance, and effectiveness of Transmit power control in reducing the radio interference in WLAN network, and thus improving link quality, and thus achieving better link throughput. Nonetheless, most of the methods based on unworkable assumptions, or a drastic change in MAC layer., failed to address the hidden node problem.
In this thesis, we present an Adaptive transmit power control algorithm to enhance the spatial reuse in wireless local area networks. Our advanced algorithm is based on actual power measurements. It reached the expectation of delivering better spatial reuse, improved link throughput, and reduced package re-transmission. Our work compensates for the lack of cutting-edge hardware by implementing the IEEE802.11 recommenda- tions ourselves. since we have implemented the neighbouring report in amendments IEEE802.11k in a different way.
6.2 Future Work
Even though our algorithm has performed well as we planned, yet, it can be improved.
One way to improve our work is being able to work as a loadable kernel model for the
Linux wireless driver. Furthermore, it would be beneficial taking frame loss and data rate adaptation into consideration. Also, testing our algorithm in a dense environment and combining it with a sort of channel selection algorithm could improve the spital reuse even higher. Further, our code can be edited by using faster programming language, besides, testing a newer wireless card that supports clients neighbouring report would make the code even faster. To overcome the hidden problem, we suggest implementing wireless card supports adjusting the clear channel assessment threshold.
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