Piggybacking an Additional Lonely Bit on Linearly Coded Payload Data

Larsson, Erik G.; Moosavi, Reza

doi:10.1109/wcl.2012.041612.120093

Cited by 19 publications

(29 citation statements)

References 8 publications

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“…Moreover, unlike HTC, in MTC the data traffic is uplink-driven with packet sizes going down as low as a few bits [7]. An example of a single-bit transmission is the transmission of ACK/NACK bits [8]. In the mMTC context, the amount of signaling overhead per packet can become very significant compared to traditional setups with mainly humandriven traffic [9].…”

Section: Introductionmentioning

confidence: 99%

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Şenel

Larsson

2018

IEEE Trans. Commun.

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284

277

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Section: Introductionmentioning

confidence: 99%

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Şenel

Larsson

2018

IEEE Trans. Commun.

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284

277

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“…There are practical solutions for transmission of a single bit of control information. For example, [21] considers the joint transmission of linearly coded payload data and a single "additional bit". The transmitter uses the additional bit, through a one-to-one mapping, to select one out of two possible codebooks for the encoding of the payload.…”

Section: A Motivationmentioning

confidence: 99%

Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things

Liu

Larsson

et al. 2018

IEEE Signal Process. Mag.

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467

321

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The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT). A generic scenario for IoT connectivity involves a massive number of machine-type connections. But in a typical application, only a small (unknown) subset of devices are active at any given instant, thus one of the key challenges for providing massive IoT connectivity is to detect the active devices first and then to decode their data with low latency. This article advocates the usage of grant-free, rather than grant-based, random access scheme for the problem of massive IoT access and outlines several key signal processing techniques that promote the performance of the considered grant-free strategy, focusing primarily on advanced compressed sensing technique and its application for efficient detection of the active devices.We show that massive multiple-input multiple-output (MIMO) is especially well-suited for massive IoT connectivity in the sense that the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station goes to infinity by using the multiple-measurement vector (MMV) compressed sensing techniques. The paper also provides a perspective on several related important techniques for massive access, such as embedding of short messages onto the device activity detection process and the coded random access.

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“…We choose the columns of A uniformly i.i.d. from the sphere of radius √ L. Notice also that if one wishes to send the same payload message using the piggybacking scheme of [26,33], each user should make use of 2 96 columns, which is totally impractical.…”

Section: Simulationsmentioning

confidence: 99%

Grant-Free Massive Random Access With a Massive MIMO Receiver

Fengler

Haghighatshoar

Jung

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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We consider the problem of unsourced random access (U-RA), a grant-free uncoordinated form of random access, in a wireless channel with a massive MIMO base station equipped with a large number M of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the M -dimensional channel vector of each user remains constant over a coherence block containing L signal dimensions in time-frequency. In the considered setting, the number of potential users Ktot is much larger than L but at each time slot only Ka Ktot of them are active. Previous results, based on compressed sensing, require that Ka ≤ L, which is a bottleneck in massive deployment scenarios such as Internet-of-Things and U-RA. In the context of activity detection it is known that such a limitation can be overcome when the number of base station antennas M is sufficiently large and a covariance based recovery algorithm is employed at the receiver. We show that, in the context of U-RA, the same concept allows to achieve high spectral efficiencies in the order of O(L log L), although at an exponentially growing complexity. We show also that a concatenated coding scheme can be used to reduce the complexity to an acceptable level while still achieving total spectral efficiencies in the order of O(L/ log L).Index Terms-Random Access (RA), Internet of Things (IoT), Massive MIMO, Grant-Free RA, Unsourced RA.

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Piggybacking an Additional Lonely Bit on Linearly Coded Payload Data

Cited by 19 publications

References 8 publications

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things

Grant-Free Massive Random Access With a Massive MIMO Receiver

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