The coming extension of cellular technology to base-stations in low-earth orbit (LEO) requires a fresh look at terrestrial 3GPP channel models. Relative to such models, sky-to-ground cellular channels will exhibit less diffraction, deeper shadowing, larger Doppler shifts, and possibly far stronger cross-cell interference: consequences of high elevation angles and extreme “sectorization” of LEO satellite transmissions into partially-overlapping spot beams. To permit forecasting of expected signal-to-noise ratio (SNR), interference-to-noise ratio (INR), and probability of outage, we characterize the powers of desired and interference signals as received by ground users from such a LEO satellite. In particular, building on the Shadowed Rician (SR) channel model, we observe that co-cell and cross-cell sky-to-ground signals travel along similar paths, whereas terrestrial co- and cross-cell signals travel along very different paths. We characterize SNR, signal-to-interference ratio (SIR), and INR using transmit beam profiles and linear relationships that we establish between certain SR random variables. These tools allow us to simplify certain density functions and moments, facilitating future analysis. Numerical results yield insight into the key question of whether emerging LEO systems should be viewed as interference- or noise-limited.

This paper offers a technique for estimating collision risk for automated ground vehicles engaged in cooperative sensing. The technique allows quantification of (i) risk reduced due to cooperation, and (ii) the increased accuracy of risk assessment due to cooperation. If either is significant, cooperation can be viewed as a desirable practice for meeting the stringent risk budget of increasingly automated vehicles; if not, then cooperation-with its various drawbacks-need not be pursued. Collision risk is evaluated over an ego vehicle's trajectory based on a dynamic probabilistic occupancy map and a loss function that maps collision-relevant state information to a cost metric. The risk evaluation framework is demonstrated using real data captured from two cooperating vehicles traversing an urban intersection.

Prior work has shown that unique non-idealities in each wireless device give rise to radiometric fingerprints in the transmitted signal. However, even a device with no such uniqueness would betray channel state information (CSI) at the wireless physical layer. A passive listener can use this CSI to de-anonymize the sender.

This paper makes the case for "Room-Area Networks" (RAN), a new category that falls between personal area networks and local area networks. In a RAN, a set of nodes can hear each other only if they are in the same room, broadly construed as being within earshot. We define a RAN abstraction, and we present example applications ranging from social contact management to building automation to gaming where this abstraction will help. The requirements of a RAN are poorly served by current technologies such as Bluetooth, near-field communication (NFC), Wi-Fi, and infrared. Acoustic channels, on the other hand, are well-suited in principle for effective propagation within human earshot and sharp attenuation at room boundaries. We provide a portable reference implementation of an 802.11a-like physical layer for the acoustic medium that works on current mobile devices, with successful communication even in noisy environments at distances over 8 meters.

Spinal codes are a recently proposed capacity-achieving rateless code. While hardware encoding of spinal codes is straightforward, the design of an efficient, high-speed hardware decoder poses significant challenges. We present the first such decoder. By relaxing data dependencies inherent in the classic M-algorithm decoder, we obtain area and throughput competitive with 3GPP turbo codes as well as greatly reduced latency and complexity. The enabling architectural feature is a novel alpha-beta incremental approximate selection algorithm. We also present a method for obtaining hints which anticipate successful or failed decoding, permitting early termination and/or feedback-driven adaptation of the decoding parameters.

Recently, rateless codes have introduced a promising approach to obtaining wireless throughput higher than what is achieved by fixed-rate codes, especially over time-varying channels. Rateless codes like Raptor, Strider, and spinal codes naturally process all the information available at the receiver corresponding to a packet, whether from one or many frame transmissions. However, a profitable deployment of rateless codes in a wireless network requires a link-layer protocol to coordinate between sender and receiver. This protocol needs to determine how much coded data should be sent before the sender pauses for feedback from the receiver. Without such feedback, an open-loop sender would not know when the packet has been decoded, but sending this feedback is not free and consumes a significant fraction of the packet transmission time. This paper develops RateMore, a protocol that learns the probability distribution of the number of symbols required to decode a packet (the decoding CDF), and uses the learned distribution in a dynamic programming strategy to produce an optimal transmission schedule. Our experiments show that RateMore reduces overhead by between 2.6x and 3.9x compared to 802.11-style ARQ and between 2.8x and 5.4x compared to 3GPP-style "Try-after-n" HARQ.

Spinal codes are a new class of rateless codes that enable wireless networks to cope with time-varying channel conditions in a natural way, without requiring any explicit bit rate selection. The key idea in the code is the sequential application of a pseudo-random hash function to the message bits to produce a sequence of coded symbols for transmission. This encoding ensures that two input messages that differ in even one bit lead to very different coded sequences after the point at which they differ, providing good resilience to noise and bit errors. To decode spinal codes, this paper develops an approximate maximum-likelihood decoder, called the bubble decoder, which runs in time polynomial in the message size and achieves the Shannon capacity over both additive white Gaussian noise (AWGN) and binary symmetric channel (BSC) models. Experimental results obtained from a software implementation of a linear-time decoder show that spinal codes achieve higher throughput than fixed-rate LDPC codes, rateless Raptor codes, and the layered rateless coding approach of Strider, across a range of channel conditions and message sizes. An early hardware prototype that can decode at 10 Mbits/s in FPGA demonstrates that spinal codes are a practical construction.