Optimistic DRX for Machine-Type Communications in LTE-A Network

Chang, Hui-Ling; Tsai, Meng-Hsun

doi:10.1109/access.2018.2791466

Cited by 16 publications

(13 citation statements)

References 16 publications

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“…Two important traffic parameters  and   used in the Markov chain of Figure.1 are expressed like [22] as, (16) Now the probability of staying in data reception mode is S 0 .…”

Section: System Modelmentioning

confidence: 99%

“…The model is helpful to measure the inclination of sleep and active state of a UE on the LTE network using the entropy of binary state. In [16] the concept of DRX is explained in a different way called optimistic DRX (ODRX), which is modeled by the finite state machine. The profiles of a power-saving factor against wakeup latency, data arrival rate and length of the cycle are shown with a comparison of DRX.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Tradeoff between Mean Delay and Energy Saving Factor under DRX Scheme

Hossain¹,

Islam²,

Tabassum³

et al. 2020

IJCA

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Recently power saving is a vital issue for wireless devices of 4G and 5G networks. A device enters in sleeping mode (short and long sleep cycle) when there is no arrival of traffic but wakeup once the arrival of traffic. Before wakeup, the UE user equipment (UE) spends the rest of the sleeping cycle which incurs a delay of service. There is a tradeoff between the length of a sleep cycle (power saving factor ids higher for longer sleep cycle) and mean delay of service. In this paper, a Markov chain is designed including timer inactivity, short sleep, and long sleep and active service states. The closedform solution of the chain is performed using node equations hence comparison of performance is made with previous work in the context of power-saving factor and mean delay. Both the power saving factor and mean delay of this paper are found marginally better than the previous work at lower packet arrival rate but at higher arrival rate performance are almost the same but claims some explanations.

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“…Two important traffic parameters  and   used in the Markov chain of Figure.1 are expressed like [22] as, (16) Now the probability of staying in data reception mode is S 0 .…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Tradeoff between Mean Delay and Energy Saving Factor under DRX Scheme

Hossain¹,

Islam²,

Tabassum³

et al. 2020

IJCA

View full text Add to dashboard Cite

show abstract

“…In cellular networks, eMBB is considered for enhancing spectral efficiency of emerging networks as network end users have insatiable demands for mobile data. Machine type communication (MTC), on the other hand, is an advanced model proposed by the 3rd generation partnership project (3GPP), where electronic devices and machines are configured to efficiently communicate with one another by the transfer of data over wireless communication networks with little or no human involvements [53][54][55][56]. Transmission latency [57][58][59] refers to the time interval (delay) that occurs when propagating the input data from the source to the destination over a communication link.…”

Section: Phy/mac Layer Design Considerations For 5gmentioning

confidence: 99%

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

et al. 2019

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The advancement in wireless communication applications encourages the use of effective and efficient channel estimation (CE) techniques because of the varying behaviour of the Rayleigh fading channel. In most cases, the emphasis of most proposed CE schemes is to improve the CE performance and complexity for ensuring quality signal reception and improved system throughput. Candidate waveforms whose designs are based on filter bank multi-carrier (FBMC) modulation techniques such as filter bank orthogonal frequency division multiplexing based on offset quadrature amplitude modulation (OFDM-OQAM), universal filtered multicarrier (UFMC) and generalised frequency division multiplexing based on offset quadrature amplitude modulation (GFDM-OQAM) are no exception to the use of these proposed CE techniques in the literature. These schemes are considered as potential waveform candidates for the physical/media access control layer of the emerging fifth generation (5G) networks. Therefore, pinpoint CE techniques represent an important requirement for these waveforms to attain their full potentials. In this regard, this paper reviews the concept of CE as applicable to these waveforms as well as other waveform candidates under consideration in the emerging 5G networks. Since the design of the majority of the waveform candidates is filter based, a review of the general filter design considerations is presented in this paper. Secondly, we review general CE techniques for candidate waveforms of next generation networks and classify some of the studied CE techniques. In particular, we classify the CE schemes used in filter bank OFDM-OQAM and GFDM-OQAM based transceivers and present a performance comparison of some of these CE schemes. Besides, the paper reviews the performances of two linear CE schemes and three adaptive based CE schemes for two FBMC based waveform candidates assuming near perfect reconstruction (NPR) and non-perfect reconstruction (Non-PR) filter designs over slow and fast frequency selective Rayleigh fading channels. The results obtained are documented through computer simulations, where the performances of the studied CE schemes in terms of the normalised mean square error (NMSE) are analysed. Lastly, we summarise the findings of this work and suggest possible research directions in order to improve the potentials of the studied candidate waveforms over Rayleigh fading channels.

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“…The main reason for non-suitability is the directional communications in 5G networks, which require beamforming operation prior to data transmission [4]. Whereas, LTE-DRX mechanism does not include beam searching operation [19,20]. Figure 3 shows the concept of directional communication with multiple beams in 5G network.…”

Section: Non-compatibility Of Lte-drx For 5g Networkmentioning

confidence: 99%

Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks

et al. 2019

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5G is expected to deal with high data rates for different types of wireless traffic. To enable high data rates, 5G employs beam searching operation to align the best beam pairs. Beam searching operation along with high order modulation techniques in 5G, exhausts the battery power of user equipment (UE). LTE network uses discontinuous reception (DRX) with fixed sleep cycles to save UE energy. LTE-DRX in current form cannot work in 5G network, as it does not consider multiple beam communication and the length of sleep cycle is fixed. On the other hand, artificial intelligence (AI) has a tendency to learn and predict the packet arrival-time values from real wireless traffic traces. In this paper, we present AI based DRX (AI-DRX) mechanism for energy efficiency in 5G enabled devices. We propose AI-DRX algorithm for multiple beam communications, to enable dynamic short and long sleep cycles in DRX. AI-DRX saves the energy of UE while considering delay requirements of different services. We train a recurrent neural network (RNN) on two real wireless traces with minimum root mean square error (RMSE) of 5 ms for trace 1 and 6 ms for trace 2. Then, we utilize the trained RNN model in AI-DRX algorithm to make dynamic short or long sleep cycles. As compared to LTE-DRX, AI-DRX achieves 69 % higher energy efficiency on trace 1 and 55 % more energy efficiency on trace 2, respectively. The AI-DRX attains 70 % improvement in energy efficiency for trace 2 compared with Poisson packet arrival model for λ = 1 / 20 .

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Optimistic DRX for Machine-Type Communications in LTE-A Network

Cited by 16 publications

References 16 publications

The Tradeoff between Mean Delay and Energy Saving Factor under DRX Scheme

The Tradeoff between Mean Delay and Energy Saving Factor under DRX Scheme

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks

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