Method, system and device for error detection in OFDM wireless communication networks

Abstract

A system, method and device for error detection/estimation in OFDM communications systems is proposed. The disclosed mechanism allows an efficient error prediction in a received data block (e.g. a packet) without using error detection codes that may impair spectral efficiency (due to the overhead) especially when very small size packets are used. In order to do that, it generates a decision variable with the aim to check whether a received block has errors or not, without resorting to the use of error-detection codes.

Claims

1. A method for detecting errors in an information block in an Orthogonal Frequency-Division Multiplexing, OFDM, network, where the information block is received by an OFDM receiver through a communication channel and contains N bits, the method characterized by comprising the following steps performed by the receiver: a) Obtaining an a posteriori log-likelihood ratio, LLR, value for each received bit of the block; b) Calculating a probability of error for the received block, BLEP.sub.0, based on the calculated LLR values; c) Calculating an average block error rate for the received block, based on post-detection Signal to Interference and Noise Ratios, SINR, of the received bits; d) Obtaining a probability density function of the block error probability, BLEP, based on the probability density function of the LLR values, where the block error probability is a random variable with a mean value given by the average block error rate; e) Calculating a decision variable value, where the decision variable value is a function of the probability density function of the block error probability and BLEP.sub.0; f) Comparing the decision variable value with a first threshold and determining whether the received block has errors based at least on a result of said comparison.

2. The method according to claim 1, where the decision variable is the probability that the block error probability, BLEP, is higher than BLEP.sub.0.

3. The method according to claim 1, where the first threshold is the calculated average block error rate.

4. The method according to claim 1 where the method further comprises calculating a block error detection reliability, LLR.sub.BlockError, and determining that the received block has errors only if said block error reliability is above a second threshold.

5. The method according to claim 4 where the block error reliability is calculated as the logarithm of the ratio between the decision variable and the average block error rate.

6. The method according to claim 1 where the LLR values are defined as: ${\mathrm{LLR}}_n\ln \frac{p\left(x_n=\text{+}1|y\right)}{p\left(x_n=1|y\right)},n=0,\mathrm{.Math.},N-1,$ where LLR.sub.n denotes the a posteriori log-likelihood ratio of the received n-th bit of the block, y denotes the received signal, x.sub.n is the corresponding n-th transmitted bit and p denotes the a-posteriori probability.

7. The method according to claim 1 where the probability density function of the block error probability, .sub.BLEP(BLEP) is calculated as a function of the probability density function of the LLR values, using the following transformation: $\ln \left(1-\mathrm{BLEP}\right)=-\underset{n=0}{\overset{N-1}{.Math.}}\ln \left(1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}\right)$ where LLR.sub.n denotes the a posteriori log-likelihood ratio of the received n-th bit of the block.

8. The method according to claim 1 where the decision variable is calculated as: $\underset{{\mathrm{BLEP}}_0}{\stackrel{1}{}}{f}_{\mathrm{BLEP}}\left(\mathrm{BLEP}\right)\mathrm{BLEP},$ where .sub.BLEP(BLEP) is the probability density function of the block error probability BLEP.

9. The method according to claim 1 where the probability of error for the received block, BLEP.sub.0 is calculated as: ${\mathrm{BLEP}}_0=1-\underset{n=0}{\overset{N-1}{.Math.}}\left(\frac{1}{1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}}\right)$ where LLR.sub.n denotes the a posteriori log-likelihood ratio of the received n-th bit of the block.

10. The method according to claim 1 where the block is transmitted by an OFDM transmitter without including any error detection bits.

11. The method according to claim 1 where the LLR values are obtained after Forward Error Correction, FEC, decoding of the received bits.

12. The method according to claim 1 where calculation of an average block error rate for the received block includes: Obtaining post-detection SINR values for each received bit of the block; From said post-detection SINR values, calculating an effective SINR by means of a Link to System technique; Calculating the average block error rate as the block error rate that would be obtained if the communication channel would have been an Additive White Gaussian Noise Channel for the effective SINR value calculated.

13. A non-transitory digital data storage medium storing a computer program comprising instructions, causing a computer executing the program to perform all steps of the method according to claim 1, when the program is run on a computer.

14. An OFDM receiver for detecting errors in an information block received through a communication channel of an OFDM network, where the information block contains N bits, the receiver comprising: A constellation symbol detector for obtaining the soft output metrics of the received bits included in the information block Means for: Obtaining an a posteriori log-likelihood ratio, LLR, value for each received bit of the block; Calculating a probability of error for the received block, BLEP.sub.0 based on the calculated LLR values; Calculating an average block error rate for the received block, based on post-detection Signal to Interference and Noise Ratios of the received bits; Calculating a probability density function of the block error probability based on the probability density function of the LLR values, where the block error probability is a random variable with a mean value given by the average block error rate; Calculating a decision variable value, where the decision variable value is a function of the probability density function of the block error probability and BLEP.sub.0; Comparing the decision variable value with a first threshold and determining whether the received block has errors based at least on a result of said comparison.

15. A system for detecting errors in an information block in a OFDM network comprising the OFDM receiver according to claim 14 and an OFDM transmitter with means for transmitting an information block through an OFDM wireless channel to the OFDM receiver.

Description

DESCRIPTION OF THE DRAWINGS

(1) For the purpose of aiding the understanding of the characteristics of the invention, according to a preferred practical embodiment thereof and in order to complement this description, the following figures are attached as an integral part thereof, having an illustrative and non-limiting character:

(2) FIG. 1 shows a schematic block diagram of a network scenario for a possible application case according to an embodiment of the invention.

(3) FIG. 2 shows the probability density function of BLEP (Block Error Probability) in order to illustrate the meaning of a proposed decision variable according to an embodiment of the invention.

(4) FIG. 3 shows a flow diagram of the processs to predict errors in a received block according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

(5) The matters defined in this detailed description are provided to assist in a comprehensive understanding of the invention. Accordingly, those of ordinary skill in the art will recognize that variation changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. Also, description of well-known functions and elements are omitted for clarity and conciseness.

(6) Of course, the embodiments of the invention can be implemented in a variety of architectural platforms, operating and server systems, devices, systems, or applications. Any particular architectural layout or implementation presented herein is provided for purposes of illustration and comprehension only and is not intended to limit aspects of the invention.

(7) The proposed invention provides a method, system and device for error determination in OFDMA communication systems (or more generically in OFDM communications sytems) as for example LTE networks or any other type of OFDM networks. The proposed mechanism allows an efficient error prediction in a received data block (e.g. a packet) without using error detection codes. In order to do that, it generates a decision variable with the aim to check whether a received block has errors or not, without resorting to the use of error-detection codes.

(8) FIG. 1 depicts a schematic block diagram of a network scenario for a possible application case according to an embodiment of the invention. In FIG. 1, there is a transmitter (10) (for example, an OFDM transmitter, that is, a transmitter able to transmit signals using an OFDM technique), a receiver (14) (for example, an OFDM receiver, that is, a receiver able to receive signals transmitted using an OFDM technique; the receiver may include a constellation symbol detector) and a communication channel (12) which connects said transmitter with said receiver (that is, the transmitter sends the signals to the receiver through the communication channel).

(9) In an embodiment, the transmitter (10) sends a block (packet) of bits B of size N (13), which represents the information (11) to be conveyed to the receiver. Said block have no error detection bits (like CRC or parity bits) included in it. The information passes through the channel (12) and arrives at the receiver (14), which firstly obtains soft output metrics (15) of the received bits containing suitable information about the associated reliabilities. Reliabilities are usually calculated in the form of a posteriori LLR (Log Likelihood Ratio) values (17). The error prediction block (16) will then find a suitable metric to decide with sufficient reliability whether the received block has errors or not. To do this, the error prediction block will, for example, apply one of the mechanisms that will be explained later.

(10) In the following reasoning, it will be assumed that the receiver is able to obtain soft-output metrics of the received bits (e.g. from the detector), in the form of bit-wise (at bit level) a posteriori log-likelihood ratios (LLRs). Said LLR may be defined by:

(11) $\begin{array}{cc}LL{R}_n\ln \frac{p\left(x_n=+1.Math.y\right)}{p\left(x_n=-1.Math.y\right)}& \left(1\right)\end{array}$
where y denotes the received signal and x.sub.n is the corresponding n-th transmitted bit. LLRs are convenient representations of the a posteriori probabilities p(x.sub.n|y) and are usually obtained by maximum a posteriori (MAP) decoders (like turbo decoders) although they can also be obtained in uncoded systems; generally speaking, any well known technique can be used for obtaining them. In this specific case, LLR for the n-th bit (LLR.sub.n) is calculated as the logarithm of the ratio of the probability that the n-th transmitted bit is +1 and the probability that the n-th transmitted bit is 1, having received a signal value y for said bit.

(12) A different value, LLR.sub.n, will be obtained for each of the received bits (which will depend on the reliabilities in the detection). The received bits are corrupted with random noise, therefore the LLR values will be random variables characterized by a certain probability density function.

(13) The traditional way to check whether a packet was received with errors involves: obtaining the hard-decision metric {circumflex over (x)}.sub.n=sgn(LLR.sub.n), where {circumflex over (x)}.sub.n is the n-th estimated bit with associated reliability equal to |LLR.sub.n|; constructing the estimated received block {circumflex over (B)} from the set of estimated bits {circumflex over (x)}.sub.n; and checking, with the aid of any suitable error detection code, whether there were errors in the estimated received block {circumflex over (B)}.

(14) In the present invention, the above stated procedure (especially the third step) does not appy as it is assumed instead that the system has no error detection capabilities (no error detection code included in the block) and block errors must be detected from the soft output estimates of the received bits. To do so, the probability that a given bit is received with error (the probability that the estimated n-th received bit {circumflex over (x)}.sub.n is different to the real n-th transmitted bit x.sub.n) may be calculated as:

(15) $\begin{array}{cc}\hat{p}\left(x_n{x}_n\right)=\frac{1}{1+{}^{.Math.{\mathrm{LLR}}_n.Math.}}& \left(2\right)\end{array}$
where LLR.sub.n is the obtained Log Likelihood Ratio for the n-th bit.

(16) This expression is used in other prior art documents, as for example, Error rate estimation based on soft output decoding and its application to turbo coding by E. Calvanese, S. Simoens, J. Boutros, Proc. IEEE Wireless Communications and Networking Conference (WCNC), 11-15 Mar. 2007, pp. 72-76.

(17) From this expression it is possible to derive the probability that a block of size N bits is in error, by simply considering the joint probability of error of the N received bits. Since bit errors can be assumed to be independent, the probability of error for the received block (packet) of N bits (the probability that the estimated received block {circumflex over (B)} is different to the real transmitted block B), denoted as BLEP (block error probability), may be written as:

(18) $\begin{array}{cc}BLEPp\left(\hat{B}B\right)=1-\underset{n=0}{\overset{N-1}{.Math.}}\left(1-p\left({\hat{x}}_n{x}_n\right)\right)=1-\underset{n=0}{\overset{N-1}{.Math.}}\left(1-\frac{1}{1+{}^{.Math.{\mathrm{LLR}}_n.Math.}}\right)=1-\underset{n=0}{\overset{N-1}{.Math.}}\left(\frac{1}{1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}}\right)& \left(3\right)\end{array}$
where (capital pi) is the multiplication operator. With this equation, the actual BLEP value for the received block (BLEP.sub.0) will be obtained.

(19) It is important to emphasize that this block error probability is not sufficient to decide whether the actual received block has errors or not, as it only gives the probability of block error. To this end, the receiver can also collect a set of post-detection signal to noise and interference ratios (SINRs), {.sub.n} (n=0 . . . N1) corresponding to the received bits. Post-detection SINR values are the SINR values characterizing the received bits after the detection stage, which depends on the type of detector employed. These SINR values are routinely obtained in the detection process using well known techniques, as for example, by means of suitable pilot subcarriers, training symbols, or any other well known prior art technique which will be appropriate for the purposes of the the present invention.

(20) The presence of non-uniform profiles of SINR values is characteristic of frequency dispersive channels, as happens for example in wireless cellular channels. Performance of the receiver in presence of frequency dispersive channels is in general worse than that in Additive White Gaussian Noise (AWGN) conditions, where the channel is flat and the signal is only corrupted by additive white Gaussian noise.

(21) The set of SINR values can be exploited to obtain a suitable average Block Error Rate (BLER) estimate through the use of appropriate Link to System techniques (also called Link Abstraction Models), like Exponential Effective SINR Mapping (EESM), Capacity Effective SINR Mapping (CESM), Logarithmic Effective SINR Mapping (LESM) or Mutual Information Effective SINR Mapping (MIESM), to name a few. Such techniques provide an effective SINR value that yields the same block error rate as the system has in AWGN conditions, through a suitable mapping function:

(22) $\begin{array}{cc}I\left(\frac{{}_{\mathrm{eff}}}{{}_1}\right)=\frac{1}{K}\underset{k=0}{\overset{K-1}{.Math.}}I\left(\frac{{}_k}{{}_2}\right)& \left(4\right)\end{array}$

(23) In the above expression .sub.eff is the effective SINR, I is the mapping function, K is the number of SINR samples that characterize the frequency response of the received channel, and .sub.1,.sub.2 are design parameters. .sub.1,.sub.2 can be adjusted to minimize the error between the BLER predicted by the model and the experimental BLER. For example, said parameters may be optimized for minimum squared error between the predicted BLER and the experimental BLER over a number M of channel snapshots, and in said case, their value will be given by the following expression:

(24) $\begin{array}{cc}\left\{{}_1,{}_2\right\}=\arg \min \left\{\frac{1}{M}\underset{i=0}{\overset{M-1}{.Math.}}{\left[\log \left({\mathrm{BLER}}_i\right)-\log \left({\mathrm{BLER}}_{\mathrm{AWGN}}\left({}_{\mathrm{eff},i}\left({}_1,{}_2\right)\right)\right)\right]}^2\right\}& \left(5\right)\end{array}$

(25) Here BLER.sub.i denotes the experimental BLER obtained for channel snapshot i, .sub.eff,i is the effective SINR for snapshot i, and BLER.sub.AWGN represents the BLER that would be obtained in AWGN conditions for a given SINR value (this will be used as the predicted, or estimate, average Block Error Rate). Minimization of the mean squared error is performed in the logarithmic domain in order to obtain minimum relative error for the BLER, in such a way that low BLER values can be estimated with low errors and vice versa.

(26) The function I is used to weight the individual SINR values after suitable scaling by parameter .sub.2, while .sub.1 is found to scale the effective SINR value to the region of interest according to the modulation and coding scheme (MCS). Both parameters are in general dependent on the MCS as well as the occupied bandwidth. They can be stored inside the receiver's memory so as to predict the average block error rate for a given set of post detection SINR measurements, {.sub.n}, that characterize the received block. In the particular case of MIESM, the function I is the bit-level Mutual Information (MIB) function, while in EESM the function I is an exponential function. The above stated process for obtaining a suitable average Block Error Rate (BLER) estimate from the obtained set of SINR values through the use of appropriate Link to System techniques is well known from the prior art; see for example Link Abstraction Models based on Mutual Information for LTE Downlink, J. Olmos, S. Ruiz, M. Garca-Lozano and D. Martn-Sacristn, COST 2100 TD(10)11052, 2-4 Jun. 2010, Aalborg (Denmark)).

(27) A priori knowledge of the average BLER can be exploited in connection with the block error probability BLEP. The latter can be considered a random variable with a probability density function that is in turn a function of the probability density function of the LLR values. Denoting BLEP as the random variable representing the block error probability (the actual BLEP value for the received block will be denoted as BLEP.sub.0), taking into account equation (3), a probability density function for the block error probability, .sub.BLEP(BLEP), can be calculated as a function of the probability density function of the LLR values, for example, with the aid of the following transformation:

(28) 0$\begin{array}{cc}\ln \left(1-\mathrm{BLEP}\right)=\ln \underset{n=0}{\overset{N-1}{.Math.}}\left(\frac{1}{1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}}\right)=-\underset{n=0}{\overset{N-1}{.Math.}}\ln \left(1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}\right)& \left(6\right)\end{array}$

(29) For given constant conditions of the communications channel, the random variable BLEP will be distributed around a mean value BLEP which must be equal to the average block error rate BLER (also called expected block error rate), as calculated by the Link to System model, which is in turn a function of the SINR values .sub.n through the effective SINR .sub.eff:
BLEP=BLER.sub.AWGN(.sub.eff)(7)

(30) Given a computed block error probability BLEP.sub.0, neither said BLEP.sub.0 nor the expected block error rate can predict whether the received block has errors or not. But, according to the probability density function of BLEP, only a fraction of its possible outcomes will lead to an actual error, and that fraction must be equal to the expected block error rate for the given channel conditions. As a result, the following decision variable can be constructed:
p(BLEP>BLEP.sub.n)(8)

(31) Equation (8) (the probability that BLEP is above BLEP.sub.0) represents the area under the tail of the probability density function of BLEP at the point BLEP.sub.0. In an embodiment, the following test can be performed: check whether said decision variable is below the average block error rate BLER.sub.AWGN(.sub.eff) calculated for the set of SINR values .sub.n through the effective SINR .sub.eff, and in the affirmative case the block is assumed to have errors, otherwise it is assumed to be correctly received:

(32) $\begin{array}{cc}\{\begin{array}{cc}p(\mathrm{BLEP}>{\mathrm{BLEP}}_0<{\mathrm{BLER}}_{\mathrm{AWGN}}\left({}_{\mathrm{eff}}\right)& .Math.\mathrm{ERROR}\\ \mathrm{else}& .Math.\mathrm{OK}\end{array}& \left(9\right)\end{array}$
or, more precisely:

(33) $\begin{array}{cc}\{\begin{array}{cc}\underset{{\mathrm{BLEP}}_0}{\stackrel{1}{}}{f}_{\mathrm{BLEP}}\left(\mathrm{BLEP}\right)\mathrm{BLEP}<{\mathrm{BLER}}_{\mathrm{AWGN}}\left({}_{\mathrm{eff}}\right)& .Math.\mathrm{ERROR}\\ \mathrm{else}& .Math.\mathrm{OK}\end{array}& \left(10\right)\end{array}$

(34) FIG. 2 illustrates the meaning of the proposed decision variable. The probability density function of BLEP yields the relative frequencies for all the possible outcomes of BLEP, and for given channel conditions (characterized by the set of SINR values .sub.n) it will be distributed around a mean value BLEP which is equal to the average block error rate, BLER (equation 7). For such channel conditions only a fraction of the possible outcomes for the random variable BLEP will lead to errors in the received block. Such a fraction can be calculated as the area under the tail of the probability density function at the point BLEP.sub.0, and must be equal to the average block error rate, BLER. Therefore a suitable test comprises checking whether said tail area, which represents the decision variable, is below or above the calculated BLER value, which represents the decision threshold.

(35) In another embodiment, the logarithm of the ratio of the tail area and the expected block error rate (which is an LLR magnitude for the block error) is used as an equivalent decision variable which will be compared with zero for error decisions. That is:

(36) $\begin{array}{cc}{\mathrm{LLR}}_{\mathrm{BlockError}}\ln \frac{p\left(\mathrm{BLEP}>{\mathrm{BLEP}}_0\right)}{{\mathrm{BLER}}_{\mathrm{AWGN}}\left({}_{\mathrm{eff}}\right)}.Math.\{\begin{array}{c}>0.Math.\mathrm{OK}\\ <0.Math.\mathrm{ERR}\end{array}& \left(11\right)\end{array}$

(37) Reliability may be given by the absolute magnitude |LLR.sub.BlockError|. LLR.sub.BlockError values with high absolute magnitude will lead to high reliability in the decision, while LLR.sub.BlockError values close to 0 will be more unreliable. Final decisions for block errors could be biased so as to minimize false positives: as an example, a system could decide that all received blocks are in error unless the decision variable yields OK (according for example to equation (9)) with reliability level above some threshold. Other criteria are also possible depending on the tolerance of the system to detection errors.

(38) It is possible to numerically obtain the probability density function of In(1BLEP), as a function of the probability density functions of the bit-wise LLR values as obtained for a given SINR. The probability density functions of the bit-wise LLR values can be stored in advance at the receiver's memory as a function of the effective SINR or the estimated BLER value. With them, it is possible to construct the probability density function for the block error probability BLEP. Numerical calculation of the area under such probability density function from the value BLEP.sub.0 up to 1 will lead to check whether the received block has errors or not, with a reliability given by |LLR.sub.BlockError|.

(39) As stated before, main applications for the proposed invention are communications with very small packet lengths (as for example machine-type communications), which therefore alleviate the complexity in obtaining the described probability density function. However application for larger packets is also possible provided that the receiver has larger computation capabilities, although the benefits of avoiding the overhead from the CRC will decrease with the packet length.

(40) FIG. 3 illustrates a flow diagram according to an embodiment of the invention, which would be operating at the receive side of an OFDM communications system. A block (31) (for example a data packet) of N bits, (x.sub.n, n=0 . . . N1) is received (not containing any additional bits for error detection) in a receiver comprising a symbol detector (for example a constellation symbol detector). Then, the receiver calculates (32) the (bit-wise) a posteriori log-likelihood ratios of the received bits after the detector, LLR.sub.n defined by:

(41) ${\mathrm{LLR}}_n\ln \frac{p\left(x_n=\text{+}1|y\right)}{p\left(x_n=1|y\right)},n=0,\mathrm{.Math.},N-1,$
where LLR.sub.n denotes the log-likelihood ratio of the received n-th bit, y denotes the received signal, x.sub.n is the corresponding n-th transmitted bit, and N is the number of bits in the received block.

(42) On the other hand, LLR values lead to an estimation of the actual block error probability for the received block (e.g. a packet), BLEP.sub.0 (35), by means of the following expression:

(43) ${\mathrm{BLEP}}_0=1-\underset{n=0}{\overset{N-1}{.Math.}}\left(\frac{1}{1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}}\right);$

(44) The set of post-detection SINR values {.sub.n} (33) that characterize the received block is also calculated (estimated). From the set of obtained SINR values {.sub.n}, the effective SINR, .sub.eff (36) can also be obtained with the aid of Link to System techniques. Said effective SINR is the SINR that yields the same average block error rate as the system has over an additive White Gaussian Noise channel. This is calculated by means of any suitable Link to System technique (as EESM, CESM, LESM, MIESM . . . ) through the expression:

(45) $I\left(\frac{{}_{\mathrm{eff}}}{{}_1}\right)=\frac{1}{K}\underset{k=0}{\overset{K-1}{.Math.}}I\left(\frac{{}_k}{{}_2}\right),$
where .sub.eff is said effective SINR, I is the mapping function, K is the number of SINR samples that characterize the frequency response of the received channel, and .sub.1,.sub.2 are parameters that may be optimized for minimum squared error between the predicted BLER and the experimental BLER.

(46) Such effective SINR yields an estimation of the average block error rate, BLER (38). The average block error rate can be estimated from said effective SINR value as the expression BLER.sub.AWGN(.sub.eff), where .sub.eff is said effective SINR and BLER.sub.AWGN represents the BLER that would be obtained in AWGN conditions for a given SINR value.

(47) With the aid of said SINR values and the calculated LLRs the receiver obtains a probability density function of the block error probability, BLEP (34). Said probability density function, .sub.BLEP(BLEP), can be calculated as a function of the probability density function of the LLR values with the aid of (using) the following transformation:

(48) $\ln \left(1-\mathrm{BLEP}\right)=-\underset{n=0}{\overset{N-1}{.Math.}}\ln \left(1+{}^{-.Math.{\mathrm{LLR}}_n.Math.}\right),$
where said block error probability, BLEP is a random variable with a mean value given by said average block error rate, BLEP=BLER.sub.AWGN(.sub.eff).

(49) With the obtained values for BLER and BLEP.sub.0 and the probability density function of BLEP, a decision variable (37) is formed as:

(50) $p\left(\mathrm{BLEP}>{\mathrm{BLEP}}_0\right)\underset{{\mathrm{BLEP}}_0}{\stackrel{1}{}}{f}_{\mathrm{BLEP}}\left(\mathrm{BLEP}\right)\mathrm{BLEP}$

(51) Then, it is determined whether the received block has errors using said decision variable. In an embodiment, this is made by checking whether the probability of having BLEP values above BLEP.sub.0 is lower than the value of the estimated average BLER (with the average block error rate BLER.sub.AWGN(.sub.eff)). Or in other words, comparing said decision variable with the average block error rate BLER.sub.AWGN(.sub.eff). In the affirmative case, it is considered that the received block has errors; otherwise the block is considered to be correctly received. If it is estimated that the received block has errors an appropriate prompt action can be triggered, as for example discarding said packet and/or sending a response to the transmitter such as a request for retransmission or a similar action (without having to wait for the application layer to react to a missing packet).

(52) The associated reliability of block error prediction may be also estimated, for example, by means of the absolute magnitude of the log-likelihood ratio of the block error, |LLR.sub.BlockError| defined by the expression:

(53) ${\mathrm{LLR}}_{\mathrm{BlockError}}\ln \frac{p\left(\mathrm{BLEP}>{\mathrm{BLEP}}_0\right)}{{\mathrm{BLER}}_{\mathrm{AWGN}}\left({}_{\mathrm{eff}}\right)}.$

(54) If the absolute magnitude of the above expression is much greater than zero then high reliability can be assumed for block error prediction, otherwise it will have low reliability.

(55) In another embodiment, the received block is considered to have errors when said decision variable p(BLEP>BLEP.sub.0) is below said average block error rate BLER.sub.AWGN(.sub.eff), and said reliability |LLR.sub.BlockError| is above a pre-determined threshold.

(56) In an embodiment, if correction codes are used, the LLR values are obtained after Error Correction decoding (for example, Forward Error Correction, FEC, decoding) of the received bits. If correction codes are not used, the LLR values are obtained after constellation symbol detection. Any well known technique for obtaining the LLR values can be used for the purposes of the present invention.

(57) The present invention can be used in any type of OFDM communication systems, especially in OFDM communication systems such as Long-Term Evolution, LTE, wireless cellular system, an IEEE 802.11, WiFi system, an IEEE 802.16, WiMAX system or any other type of OFDM communications system.

(58) Summarizing, the present invention proposes a method to predict whether a received block has errors in an OFDM communications system without resorting to the use of any error detection codes. This scenario is particularly attractive in applications with very small packets (like machine-type applications), where parity or CRC bits may represent a significant portion of the resulting packet thereby impairing spectral efficiency.

(59) The proposed invention can predict the probability of having a block error from the a posteriori log-likelihood ratios of the received bits. The a posteriori LLR values may be obtained after FEC decoding, but may also be obtained after the detector in systems without FEC capabilities. Knowledge of the post-detection SINR values characterizing the received bits can bring an estimate of the average block error rate, as obtained by Link to System techniques. From the received LLR values it is possible to derive the block error probability of the received block. This, in conjunction with the average block error rate and the probability density function of the block error probability, can be used to decide whether the received block has errors or not, along with the corresponding reliability of the decision. Such reliability can be used to bias block error decisions so as to minimize false positives; for example, the system could be designed to consider all received blocks as errors unless the decision yields an OK and the associated reliability is above some threshold.

(60) Having a decision variable for block errors is very attractive in applications with no error detection capabilities, where the receiver cannot trigger any action at physical layer level and has to wait instead for the application layer. Application of the present invention to large packets is also possible provided that the receiver has sufficient processing capabilities for computation of the probability density function of the block error probability.

(61) The proposed embodiments can be implemented as a collection of software elements, hardware elements, firmware elements, or any suitable combination of them.

(62) Note that in this text, the term comprises and its derivations (such as comprising, etc.) should not be understood in an excluding sense, that is, these terms should not be interpreted as excluding the possibility that what is described and defined may include further elements, steps, etc.

(63) A person of skill in the art would readily recognize that steps of various above-described methods can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, wherein said instructions perform some or all of the steps of said above-described methods. The program storage devices may be, e.g., digital memories, magnetic storage media such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover computers programmed to perform said steps of the above-described methods. The description and drawings merely illustrate the principles of the invention.

(64) Although the present invention has been described with reference to specific embodiments, it should be understood by those skilled in the art that the foregoing and various other changes, omissions and additions in the form and detail thereof may be made therein without departing from the scope of the invention as defined by the following claims. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention.