Model-Driven Energy-Aware Rate Adaptation M. Owais Khan, Vacha Dave, Yi-Chao Chen, Oliver Jensen, Lili Qiu, Apurv Bhartia, Swati Rallapalli The University of Texas at Austin MobiHoc 2013, Bangalore, India

Rate adaptation needs to energy-aware! Motivation Multi-antenna devices are becoming common Offer diverse rate choices # of antennas, modulation, coding, # of streams Rate adaptation – beaten to death problem? Large capacity gain, but significantly more energy! Talk Last few years have witnessed a prolific growth of multi-antenna devices or MIMO deployment. (routers, fancy smartphones, etc.) These devices offer diverse rate choices (# of antennas, streams, coding, etc.) thereby providing ability to serve high throughput traffic. This makes the rate adaptation problem increasingly interesting, and has/is being discussed widely in the wireless community However, most existing work talks about this in the context of maximizing throughput/performance. But with these multi-antenna devices energy consumption becomes a valid concern. (see table). PIC – check if the phone actually has MIMO Mode Intel TX Intel Rx Single Antenna 1.28 W 0.94 W Two Antennas 1.99 W 1.27 W Three Antennas 2.10 W 1.60 W Rate adaptation needs to energy-aware!

What’s the big deal? Fixed antenna systems are fairly simple 𝑒𝑛𝑒𝑟𝑔 𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 ∝𝐸𝑇𝑇 Energy-aware rate adaptation becomes simple Highest rate Lowest ETT Minimum energy! Can this be applied to MIMO as well? 𝑒𝑛𝑒𝑟𝑔 𝑦 𝑚𝑢𝑙𝑡𝑖−𝑎𝑛𝑡𝑒𝑛𝑛𝑎𝑠 ≫𝑒𝑛𝑒𝑟𝑔 𝑦 𝑠𝑖𝑛𝑔𝑙𝑒−𝑎𝑛𝑡𝑒𝑛𝑛𝑎 Additional hardware and RF chains But multiple data streams reduces transmission time! Talk The problem is significantly more challenging as compared to single antenna Wi-Fi. In single ant., energy consumption is directly proportional to the ETT, which makes the problem simpler since, Highest supported rate will have the smallest ETT Max. throughput also min. energy consumption

Energy vs. Tx time: the trade-off Reduce time by 68%! Reduce time by 50%! This figure compares the transmission time of a single antenna with that of using 2 or 3 antennas. The plot is based on the energy model of Intel 5300 card, which I’ll be talking about later. X-axis shows transmission time of a single antenna transmission Y-axis shows the % of transmission time that 2 and 3 antenna MIMO transmissions must reduce to have the same energy as single antenna transmission. From the figure, we see that for a single antenna transmission time of 0.2ms, using 3-antennas is beneficial only if we can reduce the transmission time by more than 68%. On the other hand, for a transmission time of 1.3ms, this reduction just needs to be 50%. So, in the best case scenario, the 3-antennas can reduce the transmission time by 66% (for same MCS as compared to single antenna) should lead to savings in energy! But the rate and # of antennas used depend on multiple factors like channel condition, wireless card and frame size. Exact rate and # of antennas depend on multiple factors Channel condition, wireless card and frame size No single setting to minimize energy Single antenna ≠ minimum energy

Hence, our work! Understand how energy consumption in these devices relates to these factors Design an energy-aware rate adaptation scheme that can minimize energy! And hence it becomes essential to have a comprehensive understanding of how energy consumption relates to these factors, And design a rate adaptation algorithm that can minimize energy according to the current n/w condition and wireless device.

Contributions Extensive power measurements for multiple 802.11n wireless adapters Derive energy model based on power measurements Propose an energy-aware rate adaptation scheme Evaluate using simulation and testbed experiments

Why Model-Driven? Why not probing? Model-driven Slow given the large search space w/ MIMO Hard to accurately measure the power of probe frames Model-driven Estimate power consumption for each rate under the current channel condition Directly select the one w/ lowest power

Power Measurement Setup Multiple wireless cards Intel 5300N series Atheros 11n Windows mobile smartphone Monsoon power monitor One reading/μs Maximum power value every 200μs Owais Please add the picture I got lost in the first line. And so will the audience. ‘Monitor’ need not be caps Picture speaks a 1000 words  Tell the cards that were used The power monitor used (monsoon)

Power Measurement Methodology Measurements at both transmitter and receiver Different configurations Frame size (250-1500 bytes) # of antennas 802.11n compliant data rates

Atheros Energy Measurements Atheros Wi-Fi transmitter Atheros Wi-Fi receiver 𝑬𝒏𝒆𝒓𝒈 𝒚 𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝𝑬𝑻𝑻 Slope of the line depends on # of antennas

Intel Energy Measurements Owais Is this is the model? This is probably just the actual measurements! Model is just table 2, isn’t it? We need the learnings from the measurements We need the table 2. We also need the MAPE formula. Intel Wi-Fi transmitter Intel Wi-Fi receiver 𝑬𝒏𝒆𝒓𝒈 𝒚 𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝𝑬𝑻𝑻 Slope of the line depends on # of antennas

Measurement-Driven Energy Model Use least-square fitting to develop energy models 𝐸 𝑡𝑥 =𝐴 ×𝐸𝑇𝑇+𝐵 𝐸 𝑟𝑥 =𝐶 ×𝐸𝑇𝑇+𝐷 where 𝐴, 𝐵, 𝐶, 𝐷 vary for different wireless cards Intel Atheros 𝐴 0.24× 𝑛 𝑡𝑥 +0.425×𝑀𝐼𝑀𝑂+1.02 0.38× 𝑛 𝑡𝑥 +0.108 𝐵 0.045× 𝑛 𝑡𝑥 +0.108 0.040× 𝑛 𝑡𝑥 +0.062 𝐶 0.30× 𝑛 𝑟𝑥 +0.61 0.142× 𝑛 𝑟𝑥 +0.30 𝐷 0.064× 𝑛 𝑟𝑥 +0.167 0.048× 𝑛 𝑟𝑥 +0.106 Based on the observations, we develop simple energy models using least-squares fitting to find the coefficients that best match the energy consumption of the different wireless cards. The parameters A..D vary across different wireless cards. You can see that the parameters although are similar but are not identical.

Error is consistently below 5%! Validating the model 𝑀𝐴𝑃𝐸=𝑚𝑒𝑎𝑛( 𝑥− 𝑥 ′ 𝑥 ) 𝑥 : actual energy consumption 𝑥 ′ : estimated energy consumption Card Transmission Reception Atheros 3.4% 1.3% Intel 0.65% 1.4% Phone 4.9% 3.6% Now once have the model, we then want to see how accurate our model is. The table shows the mean absolute percentage error of our energy model vs. the measurement data, and we see that the error is consistently below 5% indicating a close match. Error is consistently below 5%!

Energy Aware Rate Adaptation Select rate for next transmission that minimized energy! But this can be used easily for other objectives as well …

Channel State Information (CSI) Specifies amplitude and phase between tx-rx pair Measured for all subcarriers using preamble Reported once per received frame pp-SNR can be calculated as: 𝑆𝑁𝑅 𝑚 𝑀𝑀𝑆𝐸 = 𝐸 𝑠 𝑁 𝑡 𝑁 0 1 [ 𝐻 𝐻 𝐻+ 𝐸 𝑠 𝑁 𝑡 𝑁 0 −1 𝐼] −1 To achieve this goal, the protocol first obtains Channel State Information (CSI) as seen by the receiver. IEEE 802.11n standard specifies how to calculate and report CSI. CSI values are a collection of MxN matrices each of which specify the amplitude and phase between each pair of antennas. Its measured for all subcarriers using the preamble and reported once per received frame. Given the CSI values, the post-processed SNR can be calculated depending on the MIMO transmission mode used. For instance, here is the expression used for spatial multiplexing mode where we use a MMSE equalizer to calculate the pp-SNR for the mth stream. For brevity I’ve excused the formulas used for transmit diversity modes (look in the paper for CDD or STBC modes)

Compute loss rate Map pp-SNR to un-coded BER using known relationship Convert un-coded BER to coded BER Calculate frame error rate (FER)

Estimate energy consumption AP or back-end server keeps table of energy models Account for most commonly used Wi-Fi cards Get the make/model of the Wi-Fi card Explicit feedback or passive detection Compute ETT based on frame loss rate (FER) 𝐸𝑇𝑇= 𝑠𝑖𝑧𝑒 𝑟𝑎𝑡𝑒 1 (1−𝐹𝐸𝑅) Select the MCS with the minimum energy Different variations of schemes are possible Partial packet recovery (PPR) support Only the ETT calculation changes (ref. paper)

Putting it all together Measure CSI Calculate pp-SNR Calculate estimated loss rate Compute ETT Select rate minimizes energy! Move this upward.

Evaluation Trace-driven simulator Testbed Static and mobile channel traces using Intel 5300 Written in python Testbed Uses the Intel 5300 card Iwlwifi driver is modified to support rate adaptation

Simulation Methodology Developed in Python using real CSI traces Different schemes are supported Sample Rate with MIMO Effective SNR Maximum throughput Minimum energy Minimum Energy with throughput constraint Talk The simulator was coded in Python and uses real CSI traces captured from the Intel 5300 cards. We change the data rate on each of the frames using the various rate adaptation schemes. For comparison, we consider the following rate adaptation schemes: Sample rate with MIMO – default sample rate, but extended to work with MIMO functionality. It probes the network at a random rate every 10 frames and selects the rate that min. transmission time (incl. retx time). The goal here is to max. throughput without considering energy at all. Effective SNR – selects data rate based on eff. SNR derived from CSI values. Computes eff. SNR for the frame and aims to max. throughput. Max. throughput – pics MCS that maximized tput. Min. energy – pics MCS that min. energy while keeping frame delivery > 90%. Min. energy with throughput constraint. – ensures throughput is no lesser than X% of the max. throughput

Intel Transmitter (Static) 14%-24% 17%-31% 1-10% 1-2% 10%-22% 1-19% 0-1% 1-2% Energy (nJ/bit) Throughput (Mbps) Talk First we evaluate the performance under static network conditions. The traces here consist of 2000 CSI samples. As we can see, compared to the scheme that maximizes throughput, The energy-aware rate adaptation scheme consumes 14-24% less energy for the Intel card. Throughput loss for the card is 10-22%. Compares w/ Eff. SNR and Sample-rate, min. energy reduces transmitter energy by 17-31%. Throughput loss is 1-19%. Energy saving is higher and throughput saving is lower in these cases because these schemes are not optimal for either throughput or energy. ET-Put X balances throughput and energy. For example, compared w/ Max.-Tput scheme, ETput80 minimizes energy whilst ensuring at least 80% of max. throughput. It saves energy of upto 10% for Intel transmitter, while throughput reduction is within 1%. The Oracle schemes know the exact CSI of the next frame and eliminate the performance degradation caused by the prediction error. We can see that CSI prediction error causes only 1-2% more energy consumption and 1-2% throughput reduction, indicating that the impact of prediction error is small. Minimum Energy provides significant power savings

Intel Receiver (Static) 25-35% 26-42% 1-10% 1-4% 10%-26% 1-23% 0-1% 1-5% Energy (nJ/bit) Throughput (Mbps) Talk First we evaluate the performance under static network conditions. The traces here consist of 2000 CSI samples. As we can see, compared to the scheme that maximizes throughput, The energy-aware rate adaptation scheme consumes 25-35% less energy for the Intel card. Throughput loss for the card is 10-26%. Compares w/ Eff. SNR and Sample-rate, min. energy reduces transmitter energy by 26-42%. Throughput loss is 1-23%. Energy saving is higher and throughput saving is lower in these cases because these schemes are not optimal for either throughput or energy. ET-Put X balances throughput and energy. For example, compared w/ Max.-Tput scheme, ETput80 minimizes energy whilst ensuring atleast 80% of max. throughput. It saves energy of upto 10% for Intel transmitter, while throughput reduction is within 1%. The Oracle schmes know the exact CSI of the next frame and eliminate the performance degradation caused by the prediction error. We can see that CSI prediction error causes only 1-4% more energy consumption and 1-5% throughput reduction, indicating that the impact of prediction error is small.

Intel Receiver (Mobile) 29-31% 34-40% 1-16% 2-6% 15-19% 2-13% 0-2% 3-6% Energy (nJ/bit) Throughput (Mbps) Talk First we evaluate the performance under static network conditions. The traces here consist of 2000 CSI samples. As we can see, compared to the scheme that maximizes throughput, The energy-aware rate adaptation scheme consumes 14-24% less energy for the Intel card. Throughput loss for the card is 10-22%. Compares w/ Eff. SNR and Sample-rate, min. energy reduces transmitter energy by 17-31%. Throughput loss is 1-19%. Energy saving is higher and throughput saving is lower in these cases because these schemes are not optimal for either throughput or energy. ET-Put X balances throughput and energy. For example, compared w/ Max.-Tput scheme, ETput80 minimizes energy whilst ensuring atleast 80% of max. throughput. It saves energy of upto 10% for Intel transmitter, while throughput reduction is within 1%. The Oracle schmes know the exact CSI of the next frame and eliminate the performance degradation caused by the prediction error. We can see that CSI prediction error causes only 1-2% more energy consumption and 1-2% throughput reduction, indicating that the impact of prediction error is small.

Intel Receiver with PPR 26% to 28% ~9% 26% to 29% ~9% Energy (nJ/bit) Throughput (Mbps) Min. Energy saves 26-28% energy over Max. Throughput. The throughput loss is 26-28%. ETput80 saves energy 9-21% while the throughput degradation is < 9%. Moreover comparing the PPR schemes w/ non-PPR schemes, we can see that PPR schemes improve the energy by 6-23% by extracting correct symbols from partially corrupted frames. Minimum Energy provides significant power savings

Always use single-antenna systems? One of the interesting questions that can arise is that, should we just resort to using a Single Antenna! This can be more interestingly analyzed through this graph under the context of packet size. As we can see, for 1000 byte packets, Min-Energy always selects single-antenna. However, as the frame size increase, we see the multiple-antennas coming into picture a lot more. E.g. with 5000-byte packets, we see that it becomes more advantageous to use multiple antennas to minimize energy. Multiple-antennas provide energy savings for larger frames because for small frames, the preamble size dominates the total transmission time. Hence, using multiple antennas only results in small reductions in ETT, which does not offset the additional energy required to power up multiple antennas. As the frame size increases, using multiple antennas leads to larger reductions in ETT, which more than offsets this additional energy. Single Antenna performance decreases with increasing packet size

Testbed Implemented scheme on Intel Wi-Fi link 5300 driver Used tool in [Halperin10] to extract CSI from driver Static channel 200 UDP Packet of 1000 bytes each transmitted Results averaged over 10 runs Mobile channel Receiver moves away from transmitter at walking speed Results averaged over 5 runs

Static Channel 19% 6% 28% 16% 24% 11% 22% 2% Energy (nJ/bit) Throughput (Mbps) Now we see the throughput and energy consumption numbers for static experiments. As we can see, Min-Energy reduces the energy consumption by 19% for the transmitter and 28% for the receiver. The throughput reduction is 24% for tx, and 22% for the rx. ETPutX smoothly trades-off between the two objectives. ETPut80 reduces energy by 6% at an 11% throughput loss at the transmitter. For the receiver, ETPut80 reduces the energy by 16% with a throughput reduction of 2%.

Energy savings do not degrade with the channel! Mobile Channel This figure shows how the MCS changes over a mobile experiment for MaxTput and MinEnergy. MCS 0-7: use 1 antenna, 8-15 uses 2 antennas and 16-23 uses 3 antennas. In each case, num_spatial_streams = num_antennas. In region 1, where the channe is good, MaxTput transmits using all 3 antennas at MCS 22. Since MinEnergy tries to minimize energy, it uses MCS 6, the highest 1-antenna rate that can be supported by the current channel. 16.9% energy savings! As the receiver moves away from the transmitter, the channel degrades and MaxTput goes to MCS 14, while Min-Energy still uses MCS 6. Energy improvement reduces to 11.9% because MCS 14 consumes less energy. In region 3, MaxTput goes from MCS 14 to MCS 12 (2 antennas). MCS 12 consumes more energy than MCS 14 because fewer antennas (higher ETT). Min_Energy continues at MCS 6 thereby giving savings of 21%. In region 4, this increases to 20.06. Interesting to note that even though channel degrades continuously, the energy savings do not follow the trend. In fact, region 2 has the least savings while region 3 has the highest. Energy savings do not degrade with the channel!

Related Work Models based on data size [Carvahlo04], empirical study [Bala09] Neither considers effects of multiple antennas, data rates, tx power Study power consumption under different parameters[Halperin10] Do not develop energy model Energy measurement and Models Extensively studied [Bicket05, Holland01, Sadeghi02, Wong06, etc.] None of these schemes consider minimizing energy Energy based rate adaptation [Li12] Limited effectiveness of probing-based approach Rate Adaptation Power Saving Mode Optimization [Napman10, Sleepwell11, E-mili11] Complementary to our work Power Savings

Conclusion Collect and analyze extensive power measurements Derive simple energy models for transmission/reception Develop model-driven energy-aware rate adaptation scheme Experimentally show significant energy savings possible 14-37% over existing approaches PPR extensions can be even better

Thank You! apurvb@cs.utexas.edu Questions ??? Thank You! apurvb@cs.utexas.edu