Discrete Transmit Power Devices in Dense Wireless Networks: Methodology and Case Study

Suciu, Mihai Alexandru; Solc, Tomaz; Cremene, Ligia Chira; Mohorčič, Mihael; Fortuna, Carolina

doi:10.1109/access.2017.2669403

Cited by 6 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reliability and Scalability: Reliability of GFA was investigated in [24,40] by assuming a very dense deployment of access point in which all devices are able to perform power control via channel inversion, a feature that is hardly the case in practical scenarios [41]. In [42], an advanced compressed sensing technique for massive GFA connectivity was proposed, and its performance in reliable detection of simultaneously active devices investigated.…”

Section: Challenges Of Grant-free Accessmentioning

confidence: 99%

Energy-Efficient and Reliable IoT Access Without Radio Resource Reservation

Azari

Stefanović

Popovski

et al. 2021

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

One of the major challenges for Internet-of-Things applications is that the existing cellular technologies do not support the uplink IoT traffic in an energy-efficient manner. There are two principal ways for serving the uplink IoT traffic: grant-based (i.e. scheduled) and grant-free (i.e. random access). Grant-based access provides fine-grained control of reliability and latency at the cost of energy consumption required for signaling. Grant-free access removes the signaling overhead at the cost of looser control of performance in terms of reliability and latency. However, a precise analysis of reliability, latency and energy performance of grant-free access (GFA) is largely missing. This article focuses on a GFA-type protocol, in which a device transmits several packet replicas, asynchronously with respect to the other devices. Using stochastic geometry, we derive closedform expressions for reliability, delay, and energy consumption, which can be used to identify the tradeoffs among these performance parameters. In order to improve the performance of the protocol, we develop a receiver that leverages the random timing and frequency offsets among the devices in order to facilitate resolution of collisions. This is complemented by a per-device adaptive scheme that controls the number of transmitted replicas. The evaluation confirms the validity of the analysis and the potential of the proposed solution, identifying operating regions in which GFA outperforms the grant-based access.

show abstract

Section: Challenges Of Grant-free Accessmentioning

confidence: 99%

Energy-Efficient and Reliable IoT Access Without Radio Resource Reservation

Azari

Stefanović

Popovski

et al. 2021

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…The consideration of discrete power levels, which are used in many communication standards, e.g., 3GPP LTE and IS-95 [43], [44], is more realistic than assuming continuous power levels. Moreover, discrete power levels are beneficial when transmitters have limited capabilities due to hardware constraints, e.g., in machine-type communications [45]. However, optimization problems involving discrete power levels are more difficult to solve than problems with continuous power levels because of the resulting integer programming.…”

Section: A Channel Modelmentioning

confidence: 99%

Deep Learning-based Resource Allocation For Device-to-Device Communication

Lee¹,

Schober²

2020

Preprint

View full text Add to dashboard Cite

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized to maximize the overall spectral efficiency whilst maintaining the quality-ofservice (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels.Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communications systems with different design objectives.

show abstract