Random Access in C-RAN for User Activity Detection With Limited-Capacity Fronthaul

Utkovski, Zoran; Simeone, Osvaldo; Dimitrova, Tamarа; Popovski, Petar

doi:10.1109/lsp.2016.2633962

Cited by 43 publications

(38 citation statements)

References 19 publications

(42 reference statements)

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“…The potential to apply these advanced compressed sensing techniques for user activity detection has been discussed in [28]- [30]. It would be of great significance to investigate which compressed sensing algorithm is best suited for device activity detection in the massive IoT connectivity setting, in terms of the complexity of pilot sequence design, the pilot sequence length required to achieve reasonable device detection accuracy, the corresponding missed detection and false alarm probabilities performance and channel estimation performance, etc.…”

Section: Other Compressed Sensing Techniques For Device Activity mentioning

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

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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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Section: Other Compressed Sensing Techniques For Device Activity mentioning

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.

Self Cite

467

321

View full text Add to dashboard Cite

show abstract

“…A Bayesian compressed sensing algorithm is proposed in [16], where the received signals from all the BSs are concatenated at the CU followed by a joint user activity detection. By considering limited capacity of the fronthaul links between the BSs and the CU, [17] compares two schemes-centralized detection with received signal quantization and distributed detection with log-likelihood ratio (LLR) quantization-via simulations, demonstrating that centralized detection is preferred with high fronthaul capacity whereas distributed detection is preferred with low fronthaul capacity.…”

Section: A Related Workmentioning

confidence: 99%

Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO

Chen

Sohrabi

2019

IEEE Trans. Wireless Commun.

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This paper considers sparse device activity detection for cellular machine-type communications with nonorthogonal signatures using the approximate message passing algorithm. This paper compares two network architectures, massive multiple-input multiple-output (MIMO) and cooperative MIMO, in terms of their effectiveness in overcoming intercell interference. In the massive MIMO architecture, each base station (BS) detects only the users from its own cell while treating inter-cell interference as noise. In the cooperative MIMO architecture, each BS detects the users from neighboring cells as well; the detection results are then forwarded in the form of log-likelihood ratio (LLR) to a central unit where final decisions are made. This paper analytically characterizes the probabilities of false alarm and missed detection for both architectures. Numerical results validate the analytic characterization and show that as the number of antennas increases, a massive MIMO system effectively drives the detection error to zero, while as the cooperation size increases, the cooperative MIMO architecture mainly improves the cell-edge user performance. Moreover, this paper studies the effect of LLR quantization to account for the finite-capacity fronthaul. Numerical simulations of a practical scenario suggest that in that specific case cooperating three BSs in a cooperative MIMO system achieves about the same cell-edge detection reliability as a non-cooperative massive MIMO system with four times the number of antennas per BS.Index Terms-Compressed sensing, approximate message passing (AMP), massive connectivity, massive multiple-input multipleoutput (MIMO), machine-type communications (MTC).

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“…Random access and alternatives schemes and their effect on the operation of LTE and LTE-A are presented in [18], [19], [20]. In [21], the effect of random access in Cloud-Radio Access Network is considered. A Markov chain model for the calculation of the average cooperation delay in persistent relay carrier sensing multiple access scheme is presented in [22], while in [23] three on-demand cooperation strategies were proposed to manage the multiple relay access control problem to handle relay transmissions in an effective manner; see also [24].…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of a cooperative wireless network with adaptive relays

Dimitriou

Παππάς

2019

Ad Hoc Networks

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In this work, we investigate a slotted-time relay assisted cooperative random access wireless network with multipacket (MPR) reception capabilities. MPR refers to the capability of a wireless node to successfully receive packets from more than two other modes that transmit simultaneously at the same slot. We consider a network of N saturated sources that transmit packets to a common destination node with the cooperation of two infinite capacity relay nodes. The relays assist the sources by forwarding the packets that failed to reach the destination. Moreover, the relays have also packets of their own to transmit to the destination. We further assume that the relays employ a state-dependent retransmission control mechanism. In particular, a relay node accordingly adapts its transmission probability based on the status of the other relay. Such a protocol is towards self-aware networks and leads to substantial performance gains in terms of delay. We investigate the stability region and the throughput performance for the full MPR model. Moreover, for the asymmetric two-sources, two-relay case we derive the generating function of the stationary joint queuelength distribution with the aid of the theory of boundary value problems. For Email addresses: idimit@math.upatras.gr (Ioannis Dimitriou), nikolaos.pappas@liu.se (Nikolaos Pappas)the symmetric case, we obtain explicit expressions for the average queueing delay in a relay node without solving a boundary value problem. Extensive numerical examples are presented and provide insights on the system performance.

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Random Access in C-RAN for User Activity Detection With Limited-Capacity Fronthaul

Cited by 43 publications

References 19 publications

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

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

Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO

Performance analysis of a cooperative wireless network with adaptive relays

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