This paper presents Recitation, the first software system that uses lightweight channel state information (CSI) to accurately predict error-prone bit positions in a packet so that applications atop the wireless physical layer may take the best action during subsequent transmissions. Our key insight is that althoughWi-Fi wireless phys-ical layer operations are complex, they are deterministic. This en-ables us to rehearse physical-layer operations on packet bits before they are transmitted. Based on this rehearsal, we calculate a hidden parameter in the decoding process, called error event probability (EVP). EVP captures fine-grained information about the receiver’s convolutional or LDPC decoder, allowing Recitation to derive pre-cise information about the likely fate of every bit in subsequent packets, without any wireless channel training. Recitation is the first system of its kind that is both software-implementable and compatible with the existing 802.11 architecture for both SISO and MIMO settings. We experiment with commodity Atheros 9580Wi-Fi NICs to demonstrate Recitation’s utility with three representative applications in static, mobile, and interference-dominated scenar-ios. We show that Recitation achieves 33.8 % and 16 % average throughput gains for bit-rate adaptation and partial packet recov-ery, respectively, and 6 dB PSNR quality improvement for unequal error protection-based video.

Dynamic voltage and frequency scaling (DVFS) has long been used as a technique to save power in a variety of computing domains but typically not in communications devices. A fundamental limit that prevents decreasing the clock frequency is the Nyquist(-Shannon) sampling theorem, which states that the sampling rate must be twice the signal bandwidth. Recently, researchers have leveraged compressive sensing to demonstrate the possibility of decoding a sparse signal below Nyquist rate. In this work, we dramatically ex-tend the state of the art by showing how to decode non-sparse sig-nals, in particular, OFDM systems at sub-Nyquist rates. We exploit the aliasing that results from under-sampling and observe that there exists well-defined structure in terms of how OFDM signals are “folded up ” under aliasing. Based on our observations, we present Enfold, which allows existing WiFi chipsets to decode standards-compliant WiFi frames while operating at 50 % and 25 % of their rated clock rate. Our design is able to attain greater than 96 % and 83 % raw packet reception rates for moderate SNR while reducing the clock rate by 2 × and 4×, respectively. Moreover, our approach can be easily applied to other communication systems based on OFDM modulation. When evaluated on popular smartphone app traces, Enfold reduces energy consumption by up to 34%.

Abstract—Channel condition varies frequently in wireless networks. To achieve good performance, devices need rate adaptation. In rate adaptation, choosing proper modulation schemes based on channel conditions is vital to the transmission performance. However, due to the natural character of discrete modulation types and continuous varied link conditions, we cannot make a one-to-one mapping from modulation schemes to channel conditions. This matching gap causes either over-select or under-select modulation schemes which limits throughput performance. To fill-in the gap, we propose TiM (Time-line Modulation), a novel 3-Dimensional modulation scheme by adding time dimension into current amplitude-phase domain schemes. With estimation of channel condition, TiM changes base-band data transmission time by artificially interpolating values between original data points without changing amplitude-phase domain modulation type. We implemented TiM on USRP2 and conducted comprehensive simulations. Results show that, compared with rate adaptation choosing from traditional modulation schemes, TiM can improve channel utilization up to 200%.