Performance of Advanced Receiver Employing Interference Rejection Combining to Suppress Inter-Cell Interference in LTE-Advanced Downlink

Ohwatari, Yusuke; Miki, Nobuhiko; Asai, Takahiro; Abe, Toshinori; Taoka, Hidekazu

doi:10.1109/vetecf.2011.6093196

Cited by 54 publications

(37 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the absence of 'interference-awareness', the individual throughput maximizing strategy for each cell is the rank configuration [3,4]. With the 'interference-awareness' framework proposed in this work, the cells select the rank that maximizes the weighted expected mean throughput, resulting in the rank configuration [2, 2], as highlighted in Table I.…”

Section: A Interference-awareness: a Motivating Examplementioning

confidence: 99%

“…On the other hand, linear minimum mean squared error (MMSE) receivers, which can actively suppress inter-cell interference, are effective in improving the throughput performance due to their interference resilience properties [2]. MIMO transmission and reception, along with MMSE receivers, will therefore play a prominent role in improving the spectral efficiency of future local area systems [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A distributed interference-aware rank adaptation algorithm for local area MIMO systems with MMSE receivers

Mahmood

Berardinelli

Tavares

et al. 2014

2014 11th International Symposium on Wireless Communications Systems (ISWCS)

View full text Add to dashboard Cite

Typically, rank adaptation (RA) algorithms are aimed at balancing the trade-off between increasing the spatial gain and improving the interference resilience property. In this paper, we consider a small-cell/local area cellular system and propose a simple and distributed interference-aware rank adaptation algorithm aimed at maximizing the system sum throughput. The performance of the proposed algorithm is numerically evaluated in terms of the system sum throughput. Simulation results show that the proposed algorithm results in close to optimum throughput performance, and can provide up to 40% throughput gain over interference-unaware RA schemes.

show abstract

Section: A Interference-awareness: a Motivating Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A distributed interference-aware rank adaptation algorithm for local area MIMO systems with MMSE receivers

Mahmood

Berardinelli

Tavares

et al. 2014

2014 11th International Symposium on Wireless Communications Systems (ISWCS)

View full text Add to dashboard Cite

show abstract

“…In practical applications such as LTE/LTE-Advanced, the channel matrix of the serving cell can be estimated using downlink reference signals (RSs). For covariance matrix estimation, Copyright c 2012 The Institute of Electronics, Information and Communication Engineers the RS based scheme proposed in [10] is effective as an accurate estimation as described in [10] and [11]. This scheme separates the serving cell part and the interference and noise part from the covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Impact on Inter-Cell Interference of Reference Signal for Interference Rejection Combining Receiver in LTE-Advanced Downlink

Sano

Ohwatari

Miki

et al. 2012

IEICE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

SUMMARYThis paper investigates the dominant impact on the interference rejection combining (IRC) receiver due to the downlink reference signal (RS) based covariance matrix estimation scheme. When the transmission modes using the cell-specific RS (CRS) in LTE/LTE-Advanced are assumed, the property of the non-precoded CRS is different from that of the data signals. This difference poses two problems to the IRC receiver. First, it results in different levels of accuracy for the RS based covariance matrix estimation. Second, assuming the case where the CRS from the interfering cell collides with the desired data signals of the serving cell, the IRC receiver cannot perfectly suppress this CRS interference. The results of simulations assuming two transmitter and receiver antenna branches show that the impact of the CRS-to-CRS collision among cells is greater than that for the CRS interference on the desired data signals especially in closed-loop multiple-input multiple-output (MIMO) systems, from the viewpoint of the output signal-to-interference-plus-noise power ratio (SINR). However, the IRC receiver improves the user throughput by more than 20% compared to the conventional maximal ratio combining (MRC) receiver under the simulation assumptions made in this paper even when the CRS-to-CRS collision is assumed. Furthermore, the results verify the observations made in regard to the impact of inter-cell interference of the CRS for various average received signal-to-noise power ratio (SNR) and signal-to-interference power ratio (SIR) environments.

show abstract

“…Different procedures of the covariance estimation, namely reference signal (RS) based and user data based estimations have been summarized in [8], [7] and the references therein. In [9], the performance of using different estimations in LTE systems in a multi-cell interference scenario has been presented.…”

Section: Introductionmentioning

confidence: 99%

Interference estimation for multi-layer MU-MIMO transmission in LTE-Advanced systems

Bai

Badic

Iwelski

et al. 2012

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

View full text Add to dashboard Cite

The aim of this paper is to get an insight of the interference estimation for multi-layer multi-user multiple-input and multiple-output (ML-MU-MIMO) transmission for LTE-Advanced (long term evolution) systems. Different interference-aware receivers have been investigated in ML-MU-MIMO with the presence of co-layer, intra-cell and inter-cell interferences. User-specific reference signal (UE-RS) based channel estimation and various interference covariance estimation schemes in LTE-Advanced systems are investigated together analytically and numerically. Our investigation has shown the significant influence of the interference estimation schemes on the system performance at the link level. Furthermore, the simulation results have shown the advanced MMSE receiver together with UE-RS based interference estimation as the most robust receiver structure for the signal detection in ML-MU-MIMO transmission.

show abstract

Performance of Advanced Receiver Employing Interference Rejection Combining to Suppress Inter-Cell Interference in LTE-Advanced Downlink

Cited by 54 publications

References 2 publications

A distributed interference-aware rank adaptation algorithm for local area MIMO systems with MMSE receivers

A distributed interference-aware rank adaptation algorithm for local area MIMO systems with MMSE receivers

Impact on Inter-Cell Interference of Reference Signal for Interference Rejection Combining Receiver in LTE-Advanced Downlink

Interference estimation for multi-layer MU-MIMO transmission in LTE-Advanced systems

Contact Info

Product

Resources

About