Joint ML time–frequency synchronisation and channel estimation algorithm for MIMO-OFDM systems

This paper presents a novel software radio implementation for joint channel estimation, data decoding, and noise variance estimation in multiple-input multiple-output (MIMO) space division multiple access (SDMA). In contrast to many other iterative solutions, the proposed receiver is derived within the theoretical framework of a unified message-passing algorithm, combining belief propagation (BP) and the mean field approximation (MF) on the corresponding factor graph. The algorithm minimizes the region-based variational free energy in the system under appropriate conditions and, hence, converges to a fixpoint. As a use-case, we consider the high-rate packet-oriented IEEE 802.11n standard. Our receiver is implemented on a software-defined radio platform dubbed MIMONet, composed of a GNU radio software component and a universal software radio peripheral (USRP). The receiver was evaluated in real indoor environments. The results of our study clearly show that, once synchronization issues are properly addressed, the BP-MF receiver provides a substantial performance improvement compared to a conventional receiver also in real-world settings. Such improvement comes at the expense of an increase in running time that can be as high as 87. Therefore, the trade-off between communication performance and receiver complexity should be carefully evaluated in practical settings.

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“…Notice that the likelihood function in (11) exhibits a unique maximum in contrast to that in the work by [24].…”

Section: Synchronizer Designmentioning

confidence: 94%

A unified message-passing algorithm for MIMO-SDMA in software-defined radio

Kocian

Badiu

Fleury

et al. 2017

J Wireless Com Network

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“…The aforementioned algorithms [10][11][12], however, are only applicable to single-input-single-output (SISO)-OFDM systems. To make use of the space diversity provided by the antennas, recently a number of approaches have been addressed for the frequency offset estimation in MIMO-OFDM systems [13][14][15][16][17][18][19][20]. For instance, correlator-based approaches were considered in [13][14][15], which in general can not provide satisfactory estimation accuracy, especially in interference-rich scenarios.…”

mentioning

confidence: 99%

“…Alternatively, pilot assisted frequency offset estimation schemes have also been considered in [17,18], which, however, need a judicious choice of the pilots or an ingenious adaptive mechanism. Saemi et al [19] proposed a joint ML-based frequency offset and time offset estimation algorithm. The dimension of the matrices in the likelihood function in [19], however, will become too large to render practical implementations in multiuser scenarios.…”

mentioning

confidence: 99%

“…Saemi et al [19] proposed a joint ML-based frequency offset and time offset estimation algorithm. The dimension of the matrices in the likelihood function in [19], however, will become too large to render practical implementations in multiuser scenarios. In [20], Park et al proposed a curve-fitting technique and a weighting mechanism to mitigate time-varying mobile multipath channel and reduce inter-carrier-interference (ICI).…”

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confidence: 99%

“…Furthermore, a data detection procedure, based on the estimated frequency offsets, delays and angle selectivity, is also developed. Compared with previous works, the new approach possesses several attractive features: (1) it, unlike [16][17][18], does not require a specific training sequence or pilot arrangement, (2) it jointly estimates the frequency offsets, delays, and angle selectivity based on a novel FAF tree structure, the latter two of which are not only helpful in enhancing the estimation accuracy by partitioning the signals into appropriate groups, but they are also useful for time synchronization and STAs' location [21,22], (3) it estimates these parameters in a coarse-fine manner in three stages, thus avoiding the cumbersome convergence problem encountered in the previously addressed iterative algorithms [15,19], (4) it can provide satisfactory estimation accuracy and detection performance even in networks with MAIs and CCIs sharing the same frequency band, thus improving the total network throughput, and (5) the acquisition range of it can be as large as half the OFDM channel bandwidth which is much larger than that provided by the existing algorithms [14,15] and is free of an initial frequency offset acquisition [10].…”

mentioning

confidence: 99%

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Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

Fang

Chen

2009

Wireless Pers Commun

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Due to the popularity of IEEE 802.11a/g/n wireless local area networks, the high-density deployment of access points and their serious mutual interference have become a pressing concern, and made both frequency acquisition and data detection even more difficult. In addition, improving the network coverage and scalability in the mesh mode of IEEE 802.16 wireless metropolitan area network, space and frequency information can provide abundant scheduling information under the configuration. In light of this, this paper presents an antenna-array-assisted algorithm to solve these two problems in a multiuser multipleinput-multiple-output orthogonal frequency division multiplexing interference network. The algorithm begins with the estimation of three channel parameters: frequency offsets, delays and angle selectivity. To make a good use of the array signal characteristics, these three parameters are estimated in a frequency/delay-angle-frequency/delay (FAF) tree structure, in which two frequency/delay estimations and one angle estimation are employed alternatively. One special feature in the FAF tree structure is that temporal filtering or spatial beamforming is invoked between the parameter estimations to decompose signals so as to enhance the estimation accuracy. Thereafter, based on these parameter estimates, a data detection procedure is developed to mitigate both multiple access interference (MAI) and co-channel interference (CCI). Simulations show that the proposed algorithm can provide satisfactory performance even in networks with MAIs and CCIs sharing the same frequency band.

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