A novel low-complexity wireless neighbor discovery scheme, referred to as sparse orthogonal frequency division multiplexing (sparse-OFDM) is proposed. One area of application is the "Internet of Things" (IoT). The number of devices is very large while every device accesses the network with a small probability, so the number of active devices in a frame is much smaller than the total local device population. Sparse OFDM is a one-shot transmission scheme with low complexity, which exploits both the parallel channel access offered by OFDM and the bursty nature of transmissions. When the transmission delay of each device is an integer number of symbol intervals, analysis and simulation show that sparse OFDM enables successful asynchronous neighbor discovery using a much smaller code length than random access schemes.
Neighbor discovery can be formulated as multiuser detection from the perspective of physical layer processing @cite_14 . Due to the bursty traffic patterns, the number of active devices is typically orders of magnitude smaller than the total local device population. Based on this crucial observation, low-complexity neighbor discovery algorithms inspired by compressed sensing were proposed @cite_19 @cite_3 @cite_11 @cite_9 @cite_20 . These algorithms can reduce the code length by over 50 Noncoherent neighbor discovery based on energy detection can be formulated as the classical group testing problem @cite_24 . By utilizing the sparsity nature, neighbor discovery can be achieved efficiently using a low transmission overhead. A related study applies sparse graph codes to asynchronous group testing @cite_4 , where the required code length is larger than that for our proposed scheme.
@cite_14 @cite_4 @cite_9 @cite_3 @cite_24 @cite_19 @cite_20 @cite_11
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Abstract This paper studies the problem of neighbor discovery in wireless networks, namely, each node wishes to discover and identify the network interface addresses (NIAs) of those nodes within a single hop. A novel paradigm, called compressed neighbor discovery is proposed, which enables all nodes to simultaneously discover their respective neighborhoods with a single frame of transmission, which is typically of a few thousand symbol epochs. The key technique is to assign each node a unique on–off signature and let all nodes simultaneously transmit their signatures. Despite that the radios are half-duplex, each node observes a superposition of its neighbors’ signatures (partially) through its own off-slots. To identify its neighbors out of a large network address space, each node solves a compressed sensing (or sparse recovery ) problem. Two practical schemes are studied. The first employs random on–off signatures, and each node discovers its neighbors using a noncoherent detection algorithm based on group testing. The second scheme uses on–off signatures based on a deterministic second-order Reed–Muller code, and applies a chirp decoding algorithm. The second scheme needs much lower signal-to-noise ratio (SNR) to achieve the same error performance. The complexity of the chirp decoding algorithm is sub-linear, so that it is in principle scalable to networks with billions of nodes with 48-bit IEEE 802.11 MAC addresses. The compressed neighbor discovery schemes are much more efficient than conventional random-access discovery, where nodes have to retransmit over many frames with random delays to be successfully discovered. In this paper, we design a fast and efficient energy-based and asynchronous neighbor discovery protocol for the Internet of Things (IoT). In our solution, we relax the assumption of frame-level synchronization. We formulate a novel asynchronous group testing scheme and apply it to the neighbor discovery problem. We then show that our proposed scheme is able to detect the set of K active neighbors1 among a network of n nodes with codeword length and decoding complexity of Θ(K log (K) log (n)). Finally, we provide extensive simulation results to verify our theoretical guarantees. Fast and efficient discovery of all neighboring nodes by a node new to a neighborhood is critical to the deployment of wireless ad hoc networks. Different than the conventional ALOHA-type random access discovery schemes, this paper assumes that all nodes in the neighborhood simultaneously send their unique on-off signatures known to the receive node. In particular, a transmitter does not transmit any energy during an ldquooffrdquo mini-slot in its signature sequence. The received signal can be viewed as the outcome of a sequence of tests over the mini-slots, where the outcome of a test is positive if there is energy at the corresponding mini-slot from at least one neighbor, and negative if none of the neighboring nodes transmits energy during the mini-slot. The neighbor discovery problem is thus equivalent to a classical group testing problem. Two practical and scalable detection algorithms are developed from the group testing viewpoint. Unlike some previous neighbor discovery schemes using coherent multiuser detection, which are difficult to implement due to lack of training, the proposed scheme requires only non-coherent energy detection. The proposed algorithms are shown to achieve faster and more reliable discovery than existing random access schemes. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 .