Sojourn time based maximum likelihood estimator for velocity estimation in HetNets

Tiwari, Ravi; Deshmukh, Siddharth

doi:10.1109/ncc.2017.8077063

Cited by 9 publications

(15 citation statements)

References 12 publications

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“…For example, we can modify p 1 and p 2 to minimize B sum when ρ 1 and ρ 2 are fixed, 42,43 or to maximize X sum when ρ 1 and ρ 2 are adjustable, 43 or alternatively to maximize the sum log-rate log(ρ 1 ) + log(ρ 2 ) for better user fairness. 2,25,26,[44][45][46][47][48][49][50] In a MIMO system with N → ∞, maximal ratio combiner-successive interference cancellation is asymptotically approaching capacity. 24 We categorize maximal ratio combiner successive interfece cancellation as NOMA because multi-user detection (in the form of successive interference cancellation) and zeroforcing as orthogonal MA are required because single-user detection is required only.…”

Section: Zero-forcing Maximal Ratio Combiner and Maximal Ratio Combin...mentioning

confidence: 99%

“…In the analysis, flexible length time division is considered, in which we optimize the time slots for getting a better average sum rate among different cellular users. 37,[45][46][47][48]51 Firstly, we consider the equal energy constrained optimization where different powers are allocated to different users, but the sum of the total energy/frame will remain same.…”

Section: Noma and Its Limitation In Single-input Single-outputmentioning

confidence: 99%

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Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

Shankar¹,

Nandi²,

Rupani

2021

Journal of Defense Modeling & Simulation

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In this paper, we investigate the non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (M-MIMO) techniques and through simulation, and a comparison is given between the NOMA and orthogonal multiple access techniques. Integrating NOMA with M-MIMO is a very challenging task. In this paper, for a single-cell system, NOMA is integrated with a M-MIMO system for better spectral and energy efficiency. Investigation of the multiple user gain is the focus of this work because the multiple user gain supports simultaneous transmission of multiple users in the case of the M-MIMO system. In this way, the M-MIMO will provide a 100 times channel capacity increase, which results in very high data transmission rate. In the modern communication system, achieving multiple user gain is a very difficult task when channel estimation error is present. The performance of the orthogonal multiple access as well as NOMA system significantly reduced in the presence of channel estimation error. However, most of the current schemes do not work well with imperfect perfect channel state information conditions. Simulation results closely agree with the theoretical outcomes.

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Section: Zero-forcing Maximal Ratio Combiner and Maximal Ratio Combin...mentioning

confidence: 99%

Section: Noma and Its Limitation In Single-input Single-outputmentioning

confidence: 99%

Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

Shankar¹,

Nandi²,

Rupani

2021

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

show abstract

“…Therefore, sojourn time estimation becomes increasingly important in cell selection and get a lot of attention. Aiming at the frequent handovers and handoff failures caused by that result from the increase in BS density, Tiwari and Deshmukh [133] and Merwaday et al [134] both proposed a sojourn time based estimation method, accurately estimating the velocity of mobile users in HetNets. Results showed that proposed ML estimator based on sojourn time outperforms the Cramer-Rao lower bound (CRLB) based on handover count.…”

Section: ) Resource Optimizationmentioning

confidence: 99%

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

2019

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Recently, mobility has gathered tremendous interest as the users' desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifthgeneration mobile system and puts forward some potential trends in the near future. INDEX TERMS Mobility prediction, quality of service (QoS), resource reservation, handover management, the fifth-generation mobile system (5G).

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“…The sojourn time is defined as the time interval for which a particular user is served by a BS before it is handover to another BS. An estimation technique based on sojourn time for small cell Poisson network is derived in the work of Merwaday et al 31 In our previous works, 32,33 we have proposed maximum likelihood (ML)-based velocity estimation technique for HetNets. It needs be noted here that these techniques still lack in exploiting the velocity estimates for efficient mobility management.…”

Section: Related Research Workmentioning

confidence: 99%

Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets

Tiwari

Deshmukh

2019

Trans Emerging Tel Tech

Self Cite

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In this paper, we present an efficient handoff decision strategy and a mobility state detection (MSD) scheme for heterogeneous networks (HetNets). Due to base station (BS) densification, HetNets occasionally face unnecessary handovers and service failures. Since velocity plays a crucial role in handover process, its knowledge is imperative for effective mobility management. Furthermore, due to limited power in mobile devices, emphasis is in investigation of velocity estimators that are at service provider end. Thus, we propose two maximum likelihood-based velocity estimators exploiting handover count and sojourn time measurements, respectively. Our analysis illustrates that both the estimators are asymptotically unbiased and efficient. In addition, we also notice that variance of sojourn time-based estimator is smaller than that of handover count-based estimator. Considering superiority of sojourn time-based estimator, we propose a handover decision strategy to minimize unnecessary handovers. Here, we first predict the sojourn time of upcoming BSs on the basis of velocity estimated via sojourn time samples. Next, BSs whose predicted sojourn time are greater than a prespecified threshold are then considered to identify the next serving BS. Finally, we select next serving BS on the basis of minimum cost for satisfying quality-of-service parameters such as bandwidth, security, monetary cost, and available power at user device. This work also exploits estimated velocity to investigate MSD specified in LTE standards. The numerical and simulation results validate our approach by illustrating accuracy in velocity estimation, reduction in frequent handovers and service failures, and improvement in MSD.

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Sojourn time based maximum likelihood estimator for velocity estimation in HetNets

Cited by 9 publications

References 12 publications

Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets

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