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Introduction

An adaptive physical layer is required for various reasons. Some are: 1)to optimize the usage of resources. 2)to accommodate variety of user requirements and services. 3)to maximize the spectral efficiency.

We start by defining the spectal efficiency since this is critical for designing the physical layer.

  Spectral efficiency is a measure of the number of bits
 that can be carried by the channel. It is specified in bits
 per Hz either over the air or as a net useable Ethernet
 rate. A system spectral efficiency is therefore directly
 related to total available bandwidth.   

In order to design an adaptive physical layer, one needs to monitor the varying channel condition and perform dynamic link adaptation as per these conditions. Link adaptation is defined as procedure to adaptively select coding/modulation schemes based on channel condition in order to 1)maximize capacity/spectral efficiency 2)find a stable operating point for the system

The basic idea behind Link adaptation(LA) techniques is to adapt the transmission parameters to take advantage of prevailing channel conditions. The critical parameters to be adapted include modulation scheme and coding levels, but other quantities can also be taken into consideration such as power levels (as in power control), spreading factors, signaling bandwidth, and more. The principle is simple. LA techniques aim to exploit the variations of the wireless channel (over time, frequency, space) by dynamically adjusting certain key transmission parameters to the changing environmental and interference conditions observed between the base station and the subscriber station.

For example, when the received signal is strong, an intelligent system can use a higher order modulation scheme, that yields the highest transmission rate. As the signal fades due to factors such as distance from the BS and Radio Frequency Interference (RFI) or Electromagnetic Interference (EMI), error performance drops and link stability is jeopardized, the system can then shift to successively more robust modulation schemes to compensate for those conditions. Thus the transmitters can lower modulation levels to decrease the requirements of signal-to-interference-plus-noise ratio (SINR) for correct signal detection. Lowering SINR requirements increases the probability of successful reception, thus helping to meet the PER/BER objective. Evidently, link adaptation is helpful in delivering the QoS.

  In essence, adaptive modulation selects the highest
 data rate consistent with the lowest error rate therefore
 trading off capacity for quality of service.  

Modulation & Coding in Wimax

Base Station uses Adaptive Modulation and Coding to send data to the subscriber stations to optimize throughput. Base Station needs to know how the channel looks to the subscriber station so it tells subscriber station to measure the carrier to (interference plus noise) ratio. This is called channel quality indicator (CQI). There should be a room for CQI in the UL data for periodic information on CQI. Wimax supports feedback channel CQICH which can carry the SINR and MCS selection.

As suggested by Sanford ^[citation required]^, following modulation and coding schemes (MCS) can be considered for Wimax networks:

To implement link adaptation, the CQI should be available at either the transmitter or the receiver. Assuming that the receiver has this information, the Link adaptation procedure can be

1. Take the CQI (SINR/SNR/CINR) measurements at Receiver (SS).
2. Convert this information into BER information.
3. Based on the target BER as per the QoS requirements, select for each SNR/SINR/CINR, the corresponding MCS.
4. Send this predicted MCS to the transmitter(BS).
5. Transmitter(BS) selects MCS level based on receiver's predicted MCS feedback, QoS requirements and certain local measurements.

Alternatively another option is for the receiver(SS) to take the CQI measurement and send it to the transmitter(BS) and let the BS choose predict the preferred MCS.

We can have variety of parameters for consideration in order to choose a good MCS. Following channel parameters shall be taken into account here:

1)Channel quality SINR/CINR estimation for measuring link reliability

2)Channel selectivity time selectivity frequency selectivity spatial selectivity

CINR/SINR estimation using EESM method:

To estimate the performance of the demodulator in a channel with frequency selective signal and/or noise, a known method is the exponential effective SIR mapping(EESM). In a sense, the EESM is a channel-dependent function that maps power level and MCS level to SINR values in the AWGN channel domain. This allows using this mapping along with AWGN assumptions (such as effect of increase in power, CINR/MCS threshold tables) in order to predict the effect of MCS and boosting modification. The EESM method estimates the effective SINR using the following formula:

where \gamma is a vector [1,2,....n ] of the per-tone SINR values, which are typically different in a selective channel.

The SS should report the effective SINR to the BS, and have the BS decide what modulation and coding to use and with what power boosting. But this is complicated by the fact that the relationship between increase in power and increase in effective SINR is both channel-dependent and MCS-dependent. In context of EESM, this implies that for each MCS a different should be utilized, and for each such , different boosting should be considered.

The increase of \gamma_{eff} due to boosting is dependent, as can be seen below (where B denotes the boost ratio)

This implies that EESM is a two-dimensional mapping of boost level and an MCS dependent quantity () to effective SINR. However, we can simplify by observing that

which shows that given an SINR-per-tone vector it is sufficient for the BS to know the SS-specific curve relating EESM to . Both boosting and rate adaptation can be done based on the same curve, thus reducing the mapping problem to one dimension.

Channel selectivity

Time Selectivity Characterization

In order to provide the BS with an indication of how long a reported CINR value from an SS remains valid, the BS may request a time selectivity estimate using a REP-REQ message. When requested, the SS shall respond with an estimate of the time, in units of frames, that any individual CINR measurement (without averaging) is expected to remain valid. In addition, the BS may request that an SS provide estimated samples from a CINR cumulative distribution function (CDF). The BS will specify the number of CINR measurements used by the SS to determine the estimated CDF samples. The number of CINR measurements is specified in the units of frames, with one CINR measurement per frame. An estimated CDF point is determined by the SS as follows: If the BS requests an X% CDF point, denoted as CINR_X%, then the SS shall determine CINR_X% as the value for which X% of the measured CINR values are less than or equal to CINR_X%. The CINR values used to estimate the CDF points shall be measured on the preamble or on pilots / data of the first PUSC zone as instructed by the DCD message. The SS may also send time selectivity characterization reports to the BS using REP-RSP in an unsolicited fashion.

Frequency Selectivity Characterization

In order to characterize the relationship between channel frequency selectivity and link performance in a compact form, the parameters of an effective CINR/SINR versus weighting parameter curve can be sent from the SS to the BS as given above in the EESM method. When requested by the BS, the SS shall compute a quadratic approximation of an effective CINR (dB) vs. dB=10log() curve . The quadratic approximation is represented as: EESM dB(dB) = a+ bdB + csqr(dB)

Where a, b and c are the Y-intercept, linear, and quadratic par ameters, respectively, that are to be estimated by the SS. The quadratic approximation is derived by performing a curve fit to an experimentally derived EESM effective CINR versus curve. The experimental curve is obtained from:

where \hat{\gamma} = \frac{1}{N} \sum_i \gamma_i and \tilde{\gamma_i} = \frac{10 \gamma_i}{\gamma}

Note that the scaled versions of the per-subcarrier CINRs ( i' ) are used when calculating the curve parameters in order to limit the dynamic range needed to represent the parameters a, b and c. The curve parameters a, b and c, shall be calculated based on a curve fitting over the range of - 5 M) compared to conventional single-input single-output (SISO) transmission with the same total transmit power and same bandwidth. [12] suggests a good adaptive transmission technique for MIMO systems in order to enhancing the spectral efficiency keeping in mind a target BER. Following steps are taken in this technique:

1. Compute the spatial selectivity indicator

Based on the distribution of the relative condition number of the transmit/receive correlation matrices, defined as

D = max/min

with _max_ and _min_ being respectively the maximum and minimum eigenvalues of the spatial correlation matrix, and 1 D

1. Region 1 (NLOS, High AS): D lies in [1, 5.5)
2. Region 2 (NLOS, Low AS): D lies in [5.5, 25.8)
3. Region 3 (LOS, Low K-factor): D lies in [25.8, 85.8)
4. Region 4 (LOS, High K-factor): D lies in [85.8,+)

where each region defines a typical channel scenario, characterized by certain degree of spatial selectivity.

2)Choose a MIMO transmission mode

Take a combination of a MCS and one of the following MIMO techniques:

1. Beamforming (BF) with MRC receiver
2. Double space-time transmit diversity (D-STTD) with minimum mean squared error (MMSE) receiver
3. Spatial multiplexing (SM) with equal power allocation across the transmit antennas and MMSE receiver

These schemes can promise high throughput with a fixed BER performance and fixed number of Tx/Rx antennas.

Switching from one MCS to another:

We first need to decide the criterion on the basis of which switching shall take place. Here we take an example of how the system can switch from one MCS to another based on BER and SNR:

In order to comply with the constant BER criterion the threshold for switching between two coding modulation schemes is obtained by drawing horizontal line at the target BER. The line intersects the BER curves. The projections of the intersection points on the abscissa define the thresholds for switching between coding modulation schemes. The most robust coding modulation scheme, i.e. r = 1=4 convolutionally encoded QPSK modulation, is used when the SNR is between the projection of the first and the second intersection point. The information is carried by the second coded modulation scheme, i.e. r = 1=2 convolutionally encoded QPSK, when SNR is between the second and the third projection of intersection points. The coding modulation scheme with the highest spectral efficiency, i.e. 16-QAM, is used for SNR higher than the projection of the last intersection point on abscissa. When the SNR ratio is lower than the first intersection point, the target BER cannot be achieved and no information is transmitted.

REFERENCES

1. Emmanuel Grenier, Daniel Humire A brief overview of 802.16e specific features and current developments for simulating a WiMAX Network with ICS telecom
2. Planning a WiMAX network with ICS telecom nG
3. Steve Hilton, Wimax nuts and bolts: An Introduction to the IEEE 802.16e Standard
4. Dr. J. R. Sanford, Advanced Site Solutions for Wimax
5. Huseyin Arslan, Real Time Measurement, Sensing, and Learning for Cognitive Wireless Systems
6. Ran Yaniv, Danny Stopler, Tal Kaitz, Kfir Blum, IEEE C802.16e-05/141r1
7. Kevin Baum, Yufei Blankenship, Philippe Sartori, Mickael Batariere, Brian Classon, Mark Cudak, Tim Wilson, IEEE C802.16e-05/331
8. Alessandro Boch, Emanuela Falletti, Massimiliano Laddomada,Marina Mondin, Fabrizio Sellone, Report on required adaptations of radio interface and on baseband signal processing
9. Kuo-Hui Li, IEEE 802.16e-2005 Air Interface Overview
10. Kuo-Hui Li, Mobile WiMAX/3G Performance Comparison
11. Andy Jeffries, Morris Repeta, Wen Tong, David Wessel, and Peiying Zhu, New enabling technologies:Building blocks for next-generation wireless solutions
12. Antonio Forenza, Ashish Pandharipande,Hojin Kim, and Robert W. Heath, Jr., Adaptive MIMO Transmission Scheme: Exploiting the Spatial Selectivity of Wireless Channels
13. M. T. Ivrlac, T. P. Kurpjuhn, C. Brunner, and W. Utschick, Efficient use of fading correlations in MIMO systems, Proc. IEEE Veh. Technol. Conf., vol. 4, pp. 2763 2767, Oct. 2001''

Maintained by: harpreet.kaur@hsc.com Views: ###

Categories: Wimax

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