Mitigating Pilot Contamination in Massive MIMO Using Cell Size Reduction

Tebe, Parfait I.; Kuang, Yujun; Ampoma, Affum Emmanuel; Opare, Kwasi Adu-Boahen

doi:10.1587/transcom.2017ebp3189

Cited by 6 publications

(7 citation statements)

References 34 publications

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“…Simulations are performed over 5000 channel realizations using Monte Carlo. Without loss of generality, some simulation parameters from [19,20,22,23,25,26,33,34] are used throughout our simulations. We consider one cell whose radius is initially fixed at 2000 m. The system bandwidth is regarded as 20 MHz, with a carrier frequency equal to 2 GHz.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…(18) and (19) as − 2 . Moreover, it is shown in [34] that the fraction of time devoted to channel estimation is related to the cell radius R as…”

Section: Proposed Approach To Minimize the Effect Of Hardware Impairmmentioning

confidence: 99%

“….The downlink transmit power is fixed at 43 dBm; the fraction of time devoted to channel estimation is initially fixed at 0.3 ms [34], and the simulations are performed for 10 and 20 users for all the three cases. The results are depicted in Fig.…”

Section: Achievable User Data Ratementioning

confidence: 99%

“…31and (32) in the simulation to plot the user data rate against the cell radius for CLO and SLOs configurations, respectively, and Eq. 33and (34) to plot the required downlink transmit power against the cell radius for CLO and SLOs, respectively. The results are depicted in Fig.…”

Section: Effect Of Cell Size Reductionmentioning

confidence: 99%

“…11 for the required downlink transmit power. The cell radius is varied from 20 m to 2000 m, and the uplink training length is 6 10 time slots [34]. The co-channel cells user density is set to 0.005 users per square meter, and this parameter is shown in [34] not to vary as the radius of the cell of interest is reduced.…”

Section: Effect Of Cell Size Reductionmentioning

confidence: 99%

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Massive MIMO with Transceiver Hardware Impairments: Performance Analysis and Phase Noise Error Minimization

2019

KSII TIIS

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In this paper, we investigate the impact of hardware impairments (HWIs) on the performance of a downlink massive MIMO system. We consider a single-cell system with maximum ratio transmission (MRT) as precoding scheme, and with all the HWIs characteristics such as phase noise, distortion noise, and amplified thermal noise. Based on the system model, we derive closed-form expressions for a typical user data rate under two scenarios: when a common local oscillator (CLO) is used at the base station and when separated oscillators (SLOs) are used. We also derive closed-form expressions for the downlink transmit power required for some desired per-user data rate under each scenario. Compared to the conventional system with ideal transceiver hardware, our results show that impairments of hardware make a finite upper limit on the user's downlink channel capacity; and as the number of base station antennas grows large, it is only the hardware impairments at the users that mainly limit the capacity. Our results also show that SLOs configuration provides higher data rate than CLO at the price of higher power consumption. An approach to minimize the effect of the hardware impairments on the system performance is also proposed in the paper. In our approach, we show that by reducing the cell size, the effect of accumulated phase noise during channel estimation time is minimized and hence the user capacity is increased, and the downlink transmit power is decreased.

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