SNR estimation algorithm based on the preamble for OFDM systems in frequency selective channels

Ren, Guangliang; Zhang, Huining; Chen, Yilin

doi:10.1109/tcomm.2008.08.060406

Cited by 73 publications

(56 citation statements)

References 9 publications

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“…This latter method is based on the autocorrelation function given by the model of the channel (Rayleigh or Rice models). The authors of [11][12][13] also present SNR estimation methods for OFDM transmissions in frequency-selective channels. In order to avoid the need for the channel estimation, http://asp.eurasipjournals.com/content/2013/1/128 author in [11] proposes a method for a 2 × 2 multi-input and multi-output (MIMO) configuration which features a two pilot-symbols preamble and assumes that the channel coefficients are invariant over two consecutive carriers.…”

Section: Introductionmentioning

confidence: 99%

Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

Savaux

Louët

Djoko-Kouam

et al. 2013

EURASIP J. Adv. Signal Process.

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This article presents an iterative minimum mean square error-(MMSE-) based method for the joint estimation of signal-to-noise ratio (SNR) and frequency-selective channel in an orthogonal frequency division multiplexing (OFDM) context. We estimate the SNR thanks to the MMSE criterion and the channel frequency response by means of the linear MMSE (LMMSE). As each estimation requires the other one to be performed, the proposed algorithm is iterative. In this article, a realistic case is considered; i.e., the channel covariance matrix used in LMMSE is supposed to be totally unknown at the receiver and must be estimated. We will theoretically prove that the algorithm converges for a relevantly chosen initialization value. Furthermore simulations show that the algorithm quickly converges to a solution that is close to the one in which the covariance matrix is perfectly known. Compared to existing SNR estimation methods, the algorithm improves the trade-off between the number of required pilots and the SNR estimation quality.

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Section: Introductionmentioning

confidence: 99%

Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

Savaux

Louët

Djoko-Kouam

et al. 2013

EURASIP J. Adv. Signal Process.

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“…As FH is done exclusively among the same quality subcarriers, there is no reduction in throughput and transmission rate of the system. Hopping pattern for FH is not pre-defined because it is determined dynamically based on the subcarriers' quality [14][15][16], [18]. The algorithm of subcarrier allocation is described in Section 3 in more details.…”

Section: System Modelmentioning

confidence: 99%

Resource Allocation Algorithm with Dynamic Subcarrier Assignment in OFDMA-based Wireless Networks

2014

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Original scientific paperThe allocation of available resources is one of the main issues in multi-user systems. Dependence of system capacity on radio link quality is an additional obstacle of efficient resource allocation in wireless networks. Combinations of two opposite approaches -fair resource allocation and system capacity maximization are used to solve this problem in practice.This paper proposes a resource allocation method that is primarily based on assigning almost an equal bandwidth to all users. System capacity maximization is achieved by selecting the subcarriers with the best SNR values. This algorithm was developed for orthogonal frequency division multiple access (OFDMA) wireless systems. Resource allocation is done at the subcarrier level according to the weight factor that had been calculated for each user. Frequency hopping was used to increase frequency diversity and to make the system more robust to disturbance. Frequency hopping pattern is determined dynamically on the basis of SNR value of each subcarrier.The results of the proposed algorithm are compared with the water filling (WF) and proportional fairness (PF) methods. The influence of various data traffic classes on system throughput and resource allocation is also described.Key words: OFDMA, QoS, Resource allocation, Subcarrier scheduling Algoritam alokacije resursa s dinamičkim pridruživanjem podnosioca u bežičnim mrežama zasnovanim na OFDMA-u. U sustavima s više korisnika jedno od glavnih pitanja je kako podijeliti raspoložive resurse. Kod radio mreža dodatni otežavajući faktor predstavlja promjenjivost kapaciteta sustava ovisno o kvaliteti radio veze. U praksi se za raspodjelu resursa obično koriste algoritmi koji su kombinacija dvaju oprečnih pristupa, fer raspodjele resursa i maksimizacije kapaciteta sustava.U ovom radu predložena je metoda primarno bazirana na fer raspodjeli resursa. Maksimizacija kapaciteta sustava ostvarena je odabirom podnosilaca s najboljim mogućim SNR-om. Algoritam je razvijen za sustave bazirane na OFDMA. Dodjela resursa korisnicima vrši se na razini pojedinog podnosioca prema izračunatom težinskom faktoru za svakog korisnika posebno. Kako bi se povećao frekvencijski diverziti i sustav učinio otpornijim na smetnje, uvedeno je frekvencijsko skakanje prema dinamički odreenom predlošku. Predložak se formira na osnovu SNR vrijednosti odreene po svakom podnosiocu.Rezultati predloženog algoritma usporeeni su s WF (water filling) i PF (proportional fairness) algoritmima. Prikazan je utjecaj različitih klasa prometa na prijenosni kapacitet i raspodjelu resursa sustava.

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“…Both parameters can be directly derived from the client's channels to the AP. The client's SNR can be easily computed using the preamble [27] [29]. As for the direction, a client is received along the direction of its channel vector, i.e., h, where the elements of h are the channels from the client to the AP's antennas.…”

Section: Learning a Client's Direction And Snr Passivelymentioning

confidence: 99%

Rate adaptation for 802.11 multiuser mimo networks

Shen

Tung

Lee

et al. 2012

Proceedings of the 18th Annual International Conference on Mobile Computing and Networking

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In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively.

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SNR estimation algorithm based on the preamble for OFDM systems in frequency selective channels

Cited by 73 publications

References 9 publications

Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

Resource Allocation Algorithm with Dynamic Subcarrier Assignment in OFDMA-based Wireless Networks

Rate adaptation for 802.11 multiuser mimo networks

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