Partially Loaded Superimposed Training Scheme for Large MIMO Uplink Systems

Garg, Navneet; Jain, Anmol; Sharma, Govind

doi:10.1007/s11277-018-5639-z

Cited by 9 publications

(16 citation statements)

References 5 publications

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“…7, after each iteration, the data of more active devices can be recovered and verified correctly, its impact on channel estimation is smaller, and the performance of the channel estimation is better. At high SNR, the MSE performance of the channel estimate is very close to the ideal theoretical value (19). In this case, the data symbols has negligible effect on the channel estimation.…”

Section: B Channel Estimation and Data Recovery Performance Changes mentioning

confidence: 57%

“…When SNR is high enough, it is reasonable to assume that the impact from residual error of data recovery on the channel estimation is negligible as we mentioned in the former section. At this point MSE is given approximately by (19). According to (27), ρ eff can be represented as a function of λ…”

Section: Data Recovery Algorithmmentioning

confidence: 99%

“…According to (31), because T N c , the power that needs to be allocated to the training part is only a small percentage. However, for low SNR case, FER is inevitably high, MSE is larger than (19). In order to reduce the influence of channel estimation error on data recovery, more energy VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Data Recovery Algorithmmentioning

confidence: 99%

“…This point has been discussed and simulated completely in [23]- [25]. A few articles discussed multi-user issues [18], [19], but they are limited to dozens of devices. In [23], more devices can be supported, but the number of supported devices is still less than the number of antennas.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Channel Estimation and Data Detection Based on Superimposed Training for Many Access MIMO System in Uplink

2020

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Massive Multiple Input Multiple Output (MIMO) has great potential to improve spectrum efficiency in the fifth generation (5G) wireless communication systems. However, the efficiency was disastrously reduced by the heavy burden of overhead for device detection and channel estimation of the large amount of small data packets in the uplink channel. In the paper, we proposed a novel transmission scheme by superposing the training symbols for active device detection and channel estimation on the data symbols in the uplink transmission to improve the efficiency. More specifically, in order to mitigate the cross interference among the superposed signals, we proposed to split the transmission into the training phase and the traffic phase. Then we superposed the training phase in the next transmission with the traffic phase in the current transmission. Furthermore, we give the optimization of power allocation ratio between training phase and traffic phase to obtain the optimal overall performance. The analytic and simulation results show that, with the help of spatial isolation among devices, our proposed transmission scheme can significantly improve the transmission efficiency compared with the existing schemes.

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Section: B Channel Estimation and Data Recovery Performance Changes mentioning

confidence: 57%

Section: Data Recovery Algorithmmentioning

confidence: 99%

Section: Data Recovery Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous Channel Estimation and Data Detection Based on Superimposed Training for Many Access MIMO System in Uplink

2020

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“…be the concatenated channel vectors for K k users connected to the k th BS. The presence of massive MIMO BSs allows to utilize the channel hardening effect [28],…”

Section: Maximal Ratio Transmission (Mrt)mentioning

confidence: 99%

Function Approximation Based Reinforcement Learning for Edge Caching in Massive MIMO Networks

Garg

Sellathurai

Bhatia

et al. 2021

IEEE Trans. Commun.

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Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

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Generalized Superimposed Pilot Enabled URLLC in the Finite Blocklength Regime

Zhou,

Zhu,

Xia

et al. 2023

2023 IEEE Globecom Workshops (GC Wkshps)

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Conventional regular pilot (RP) scheme is not applicable for ultra-reliable and low-latency communication (URLLC) due to the impact of finite blocklength. In this paper, we propose to use generalized superimposed pilot (GSP) scheme for URLLC transmission in massive multi-input multi-output (mMIMO) systems. Distinguishing from the existing superimposed pilot (SP) scheme, the GSP scheme eliminates mutual interference between the pilot and data, where the data length is optimized, and the data symbols are precoded to spread over the whole transmission block. With the GSP scheme, we first formulate a weighted sum rate maximization problem by jointly optimizing the data length, pilot power, and data power and then derive closedform results, including optimal data length and achievable rate lower bound with maximum-ratio combining (MRC) detector. Based on the closed-form results, we provide the corresponding iterative algorithm where the problem is transformed into the geometry program format by using log-function approximation method. Finally, the performance of the RP, SP, and GSP schemes are compared through simulation results, which reflect the superiority and robustness of the GSP scheme in URLLC scenarios.

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Partially Loaded Superimposed Training Scheme for Large MIMO Uplink Systems

Cited by 9 publications

References 5 publications

Simultaneous Channel Estimation and Data Detection Based on Superimposed Training for Many Access MIMO System in Uplink

Simultaneous Channel Estimation and Data Detection Based on Superimposed Training for Many Access MIMO System in Uplink

Function Approximation Based Reinforcement Learning for Edge Caching in Massive MIMO Networks

Generalized Superimposed Pilot Enabled URLLC in the Finite Blocklength Regime

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