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Fine-grained Spectrum Adaptation in WiFi Networks

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Presentation on theme: "Fine-grained Spectrum Adaptation in WiFi Networks"— Presentation transcript:

1 Fine-grained Spectrum Adaptation in WiFi Networks
Sangki Yun, Daehyeok Kim and Lili Qiu University of Texas at Austin ACM MOBICOM 2013, Miami, USA

2 Is wide channel always better?
Current trend in WiFi Wireless applications increasing throughput demand Channel width is increasing Benefit of wide channel: higher throughput 802.11a/b/g 20MHz 802.11n 40MHz 802.11ac 160MHz Wireless demand i Is wide channel always better?

3 Disadvantage of wideband channel
High framing overhead High energy consumption Lower spectrum efficiency due to frequency diversity data ACK channel access preamble SIFS wide channel wide channel transmission idle period

4 Lessons Static spectrum access (wide or narrow spectrum exclusively) is insufficient Need dynamic spectrum access to get the best of both worlds Our lessons from the previous study on the channel width is that it is not sufficient to statically use either narrow or wide channel because both ones have their own advantages and disadvantages, and in order to achieve the benefit of both cases, the spectrum should be dynamically assigned.

5 Ideal case: per-frame adaptation
Clients select channel based on their preference AP needs per-frame spectrum adaptation to communicates with different clients Preferred channel may change over time -> further increase the need for per frame adaptation 20MHz time 5MHz 10MHz 20MHz Spectrum efficiency Energy efficiency

6 Challenges Enable per-frame spectrum adaptation
Sender and receiver agree on the spectrum Dynamically allocate spectrum efficiently However, achieving such a dynamic spectrum access is very challenging. First, changing the spectrum is costly because if we try to do it in hardware level, it causes delay of at least a few microseconds. Also, it requires to send and receive frames from multiple spectrums using one radio hardware. The third thing is how to make the agreement on which spectrum to use. The receiver should know which spectrum is used before starting the symbol decoding, but if we introduce new control frame or control channel for this, its overhead will reduce the gain by adapting the spectrum. Also, we need an efficient algorithm to dynamically allocate the spectrum

7 Related work Dynamic spectrum access (WiMAX, LTE, FICA)
Requires tight synchronization among clients Significant signaling overhead Spectrum adaptation (SampleWidth, FLUID) Focus on spectrum allocation and ignore spectrum agreement Slow to adjust the channel width WiFi-NC Channel width is fixed to 5MHz Requires longer CP to reduce guard bandwidth IEEE ac RTS/CTS for dynamic bandwidth management Not fine grained (minimum channel width 20MHz) There are some related work that addresses spectrum adaptation. At first, in cellular network, dynamic spectrum access is achieved based on OFDMA channel access mechanism. However, they require tight synchronization among clients and they incur significant signaling overhead, which is not suitable for wireless LAN environment. Also, SampleWidth and FLUID show the benefit of adapting the channel width. It changes the channel width in hardware level, so it pays some overhead by adapting the channel. Also, it does not take the spectrum agreement problem into account and assume the channel width is known. WiFi NC divides a spectrum into multiple 5Mhz channels, which is less flexible than FSA. Though they use very narrow guard band to reduce the overhead by using narrow channel, it requires to use longer cyclic prefix, which increases the overhead in time domain. IEEE ac introduces the concept of dynamic bandwidth management, but it relies on RTS/CTS for that. Also, the minimal channel width supported in it is 20MHz. Fortunately, the detection preamble design of ac is not changed, so FSA can complement ac to utilize narrow channel without modification in the standard.

8 FSA: Fine-grained spectrum adaptation
Per-frame spectrum access Change spectrum per-frame Communicate with multiple nodes on different subbands using one radio In-band spectrum detection using existing preamble Efficient spectrum allocation In this work, we propose a new system design that enables fine-grained spectrum adaptation that resolves the challenges in the previous slide. It’s backward compatible with existing standard, so based on narrowband communication supported in a, it adapt the channel width among 5, 10, 20MHz every frame. Also, its baseband design allows to use multiple spectrums simultaneously using one radio. Also, the spectrum detection algorithm does not require any control message overhead and preamble design changes. Finally, the spectrum allocation algorithm introduced here increases the benefit by frequency diversity.

9 Transmitter design . . . . . . . . . PHY encoder upsampler LPF
20MHz bandwidth OFDM signal Reduces bandwidth Interpolation & remove images Center frequency shifting PHY encoder upsampler LPF CF shift RF mixer PHY encoder upsampler LPF CF shift

10 Generating narrowband signals
Convert 5 or 10MHz signal based on 20MHz signal through upsampling and low pass filtering LPF upsampling frequency 20MHz Narrowband signal 20MHz frequency 20MHz signal Upsampling generates images outside tx band frequency 20MHz

11 Sending signals together
Center frequency shifting is performed and the signals in different spectrum are added 20Hz Narrowband signal 𝑠 10 [𝑛] 20Hz Shifted signal 𝑠 10 𝑓𝑠 𝑛 Center frequency shifting 𝑠 10 𝑓𝑠 𝑛 = 𝑠 10 [𝑛] 𝑒 𝑗2𝜋∆ 𝑠 𝑛 = 𝑠 10 𝑓𝑠 𝑛 + 𝑠 5 𝑓𝑠 𝑛 adding another narrowband signal Deliver to RF RF 20Hz 20Hz Mixed signal 𝑠[𝑛]

12 Receiver design LPF down-sampler PHY decoder RF . . . . . . . . . LPF
CF shift LPF down-sampler PHY decoder RF Spectrum detector . . . . . . . . . CF shift LPF down-sampler PHY decoder

13 Spectrum detector is key component
Receiver design CF shift LPF down-sampler PHY decoder RF Spectrum detector . . . . . . . . . CF shift LPF down-sampler PHY decoder Spectrum detector is key component

14 Spectrum detector Goal: Receiver identifies the spectrum used by the transmitter Possible solutions Use control channel or frame Too much overhead Target for attack Control channel may not be always available  further increase overhead Design special preamble [Eugene,12] Deployment issue The goal of the spectrum detector is to identify which spectrum is used before the receiver starts to decode the signals. For the spectrum detection, the conventional solution is relying on additional control channel or control frame such as RTS/CTS. This incurs too much overhead, and it can be easily targeted for attack by malicious users. Also, the congestion in the control channel can further increase the overhead. Another solution is designing a special preamble for the spectrum detection purpose, but it will lose the backward compatibility so it will be difficult to difficult to deploy the system.

15 Spectrum detection using STF
It is ideal to detect spectrum using existing frame detection preamble (STF) One solution: Spectral and Temporal analysis of the detection preamble (STD) Power spectral density to detect the total spectrum width Temporal analysis to identify exact spectrum allocation Costly and inaccurate especially in noisy channel Our approach Exploit special characteristics of STF for spectrum detection One possible way to detect the spectrum without control overhead and special preamble is performing the spectral density analysis in the frequency domain to identify the received signal spectrum, and perform temporal analysis to check how many frames are transmitted together in the spectrum. Our evaluation result shows it performs so bad in noisy channel condition. So instead, we propose a novel detection algorithm that exploits the special property of the detection preamble.

16 We exploit the subcarrier interval for the spectrum detection!
Characteristic of STF Time domain: 10 repetitions of 16 signals Frequency domain: 12 spikes out of 64 subcarriers with 4 subcarrier intervals t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 We exploit the subcarrier interval for the spectrum detection!

17 Spectrum detector design (Cont.)
Depending on the transmitter spectrum width, the received STF has various subcarrier intervals 20MHz Subcarrier interval: 4 Our main observation about the subcarrier interval is the interval changes depending on the channel width difference between transmitter and receiver. Given the 20MHz receiver, 10MHz Subcarrier interval: 2 5MHz Subcarrier interval: 1

18 Spectrum detection using STF
20MHz transmitter to 20MHz receiver 20MHz receiver 20MHz transmitter 20MHz STF in the frequency domain at the 20MHz receiver

19 Spectrum detection using STF
10MHz transmitter to 20MHz receiver Two subcarriers of 10MHz transmitter is merged into one subcarrier of 20MHz receiver 20MHz 20MHz receiver 10MHz transmitter STF in the frequency domain at the 20MHz receiver

20 Spectrum detection using STF
5MHz transmitter to 20MHz receiver 20MHz 20MHz receiver 5MHz transmitter STF in the frequency domain at the 20MHz receiver

21 Spectrum detection using STF
The subcarrier interval difference let us easily identify the spectrum 20MHz receiver 20MHz STF in the frequency domain at the 20MHz receiver 20MHz receiver 20MHz transmitter 20MHz

22 Spectrum detector design (Cont.)
10MHz Transform spectrum detection into pattern matching. 5MHz Our main observation about the subcarrier interval is the interval changes depending on the channel width difference between transmitter and receiver. Given the 20MHz receiver, 10MHz 10MHz 10MHz 5MHz 5MHz

23 Spectrum detector design
Cross-correlation check Maximum likelihood pattern matching RF-frontend preamble detection FFT-64 spectrum detection Received signal sampled in 20MHz rate Magnitude of 64 subcarriers Optimal Euclidean distance based spectrum detection Binary detection 𝐗 =arg min 𝑖 𝑘= 𝑦 𝑘 − 𝑥 𝑖 𝑘 𝐗 =arg min 𝑖 𝐗 𝑖 ⊕𝐘

24 Spectrum Allocation AP AP AP Controller client client client client
buffer AP AP AP Now that we have the capability of fine-grained spectrum access, the next important question to ask is how to allocate spectrum to maximize efficiency? In this paper, we focus on optimize spectrum allocation for the downlink traffic, which is the dominant traffic. We consider the following architecture where there is a controller that performs optimization for all APs. client client client client

25 Spectrum Allocation (Cont.)
Input Destinations of buffered frames CSI between APs and clients Conflict graph Goal: Minimize finish time Avoid interference Harness frequency diversity Knobs Spectrum Schedule AP used for transmission The controller takes the input of buffered frame destination, CSI between Aps and clients, and conflict graphs between different links in the graph, and tries to minimize the finish time of sending all buffered frames. In order to achieve this goal, we need to avoid interference and harness frequency diversity. We optimize finish time by selecting appropriate spectrum, schedule, and AP to use for transmission.

26 Spectrum allocation (Cont.)
Break a frame into mini-frames Break the entire spectrum into mini-channels Greedily assign a mini-frame to a mini-channel that minimizes the overall finish time while avoiding interference Find a swapping with an assigned mini-frame that leads to the largest improvement, go to step 3 1) To take advantage of fine-grained spectrum access, we … [just read the slide]

27 Evaluation methodology
Implemented testbed in Sora 2.4GHz 20MHz maximum bandwidth Evaluates detection accuracy and latency, spectrum allocation performance in testbed Trace based simulation for spectrum allocation in large-scale network

28 Spectrum detection accuracy

29 Spectrum detection delay
Median detection delay 4.2 us < detection delay budget

30 Throughput evaluation – no interference
FSA improves throughput by exploiting frequency diversity

31 Throughput evaluation – interference
In the another experiment, we added an interferer that sends signal in 2MHz narrowband. When the channel width is fixed, the client cannot avoid this narrowband interference, so the throughput is seriously reduced. When FSA is applied, the clients can effectively avoid the interference, so the impact of interference is marginal. In this evaluation, our scheme shows 110% higher throughput than the fixed channel case. With narrowband interference, the gain grows larger

32 Summary FSA – a step towards enabling dynamic spectrum access
Flexible baseband design Fast and accurate channel detection method Spectrum adaptation

33 Q & A Thank you!

34 Comparison with WiFi-NC
We performed extensive simulation to compare the performance of FSA with WiFi-NC. Here, we simulated a fading channel with RMS of delay spread is 100ns. In this simulation, FSA gives 20% higher throughput than WiFi-NC. There are two reasons for this. First, WiFi-NC uses longer CP which has larger constant overhead. Also, its signal quality was more degraded in fading channel because of the side effect of using sharp filter. Simulation in fading channel width RMS of delay spread = 100 ns WiFi NC incurs lower SNR due to sharp filtering

35 Discussion Detection accuracy Antenna gain control
Bi-directional traffic

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