Opportunistic routing based on partial CSI in MIMO random ad-hoc networks

Richter, Yiftach; Bergel, Itsik

doi:10.1186/s13638-021-02002-5

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…The simulation environment consists of 1000 sensor nodes and it is experimented for 10 runs. The proposed and existing approaches such as network on chip structure [3], opportunistic routing [20] and RL based adaptive routing [30] are compared. It is evaluated using the metrics such as energy, data delivery and data loss rate, latency and throughput.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

Chang

2023

SCPE

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The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

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Section: Simulation Resultsmentioning

confidence: 99%

“…However, the EE still needed to be improved. In [20], opportunistic routing (OR) was introduced for multiple input multiple output WANs. It also fails to reduce the latency.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

Chang

2023

SCPE

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“…In recent years, much attention has been paid to the analysis of random wireless networks 1‐4 . In particular, the homogeneous Poisson point process (HPPP, eg, References 5‐8), has gained much popularity for the modeling of the nodes positions. This model, in which the number of users in any finite area has a Poisson distribution and their locations are uniformly distributed over the area, offers a simple and useful description of an unknown network.…”

Section: Introductionmentioning

confidence: 99%

Optimal and sub‐optimal rate and power allocation for random networks

Zucker

Bergel

2023

Trans Emerging Tel Tech

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We consider the power and rate allocation for a transmission in a wireless network where the transmitter knows the channel state of the desired receiver but only knows the statistics of the interferes. While this scenario is often encountered in practice, and extremely important in dense networks, its optimal solution was not yet known. The analysis considers nodes that are distributed according to a homogeneous Poisson point process, and hence applies both to cellular and to ad-hoc networks. We derive (for the first time) the optimal power allocation at a node, based only on the channel gain to its desired receiver. We also derive the optimal rate allocation in networks that are characterized by the outage rate model. Considering sub-optimal allocations, we advocate the use of the simple threshold scheduling scheme for power allocation and linear rate adaptation for rate allocation. We prove that these suboptimal schemes are close to optimal, for any channel fading. We also present simulation results over Rayleigh fading channels that show a maximum loss of 1.5% between the advocated schemes and the optimal schemes.

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The Effect of Spatial Multiplexing of the Interference on MIMO Communication Performance

Richter

Bergel

2020

IEEE Access

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This work sheds light on the effects of spatially multiplexed interference on multiple-inputmultiple-output (MIMO) networks. In particular, any increase in the number of interfering data streams (while keeping the total interference power constant) is shown to degrade the quality of the interfered link. Although this statement appears very intuitive, it has yet to be proven. In this work, we first give a mathematical definition of the intuitive notion of 'increasing the number of streams' leads to proof that the achievable rate of a link decreases when any of its interferers increases its number of data streams. The achievable rate is measured by the mutual information of the link or by the spectral efficiency of the optimal linear Minimum-Mean-Square (MMSE) receiver. Correlatively, we show that the worst power allocation for an interferer is the equal power allocation for all the streams. This result highlights the importance of the optimization of the number of data streams at each transmitter in MIMO networks. INDEX TERMS Multiple-input-multiple-output (MIMO), Wireless communication.

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Opportunistic routing based on partial CSI in MIMO random ad-hoc networks

Cited by 5 publications

References 41 publications

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

Optimal and sub‐optimal rate and power allocation for random networks

The Effect of Spatial Multiplexing of the Interference on MIMO Communication Performance

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