This invention relates to decision feedback equalization (DFE) for a multiple-input multiple-output (MIMO) data transmission or storage system with hybrid automatic repeat request (HARQ).
In a data transmission or storage system, it is desirable for information, often grouped into packets, to be accurately received at a destination. A transmitter at or near the source sends the information provided by the source via a signal or signal vector. A receiver at or near the destination processes the signal sent by the transmitter. The medium, or media, between the transmitter and receiver, through which the information is sent, may corrupt the signal such that the receiver is unable to correctly reconstruct the transmitted information. Therefore, given a transmission medium, sufficient reliability is obtained through careful design of the transmitter and receiver, and of their respective components.
There are many strategies for designing the transmitter and receiver. When the channel characteristics are known, the transmitter and receiver often implement signal processing techniques, such as transmitter precoders and receiver equalizers, to reduce or remove the effects caused by the channel and effectively recover the transmitted signal. Intersymbol interference (ISI) is one example of a channel effect that may be approximately eliminated using signal processing.
However, not all sources of signal corruption are caused from deterministic sources such as ISI. Non-deterministic sources, such as noise sources, may also affect a signal. Due to noise and other factors, signal processing techniques may not be entirely effective at eliminating adverse channel effects on their own. Therefore, designers often add redundancy in the data stream in order to correct errors that occur during transmission. The redundancy added to the data stream is determined based on an error correction code, which is another design variable. Common error correction codes include Reed-Solomon and Golay codes.
One straightforward way to implement a code is to use forward error correction (FEC). The transmitter encodes the data according to an error correction code and transmits the encoded information. Upon reception of the data, the receiver decodes the data using the same error correction code, ideally eliminating any errors.
Another way to implement a code for error correction is to use automatic repeat request (ARQ). Unlike FEC, ARQ schemes use error-detecting rather than error-correcting codes. The ARQ transmitter encodes data based on an error-detecting code, such as a cyclic redundancy check (CRC) code. After decoding the data based on the error-detecting code, if an error is detected, the receiver sends a request to the transmitter to retransmit that codeword. Thus, ARQ protocols require a forward channel for communication from transmitter to receiver and a back channel for communication from receiver to transmitter. Ultimately, the receiver will not accept a packet of data until there are no errors detected in the packet.
Finally, FEC and ARQ may be combined into what is known as hybrid automatic repeat request (HARQ). HARQ typically uses a code that is capable of both error-correction and error-detection. For example, a codeword may be constructed by first protecting the message with an error-detecting code, such as a CRC code, and then further encoding the CRC-protected message with an error-correcting code, such as a Reed-Solomon, Golay, convolutional, turbo, or low-density parity check (LDPC) code. When the receiver receives such a code, the receiver first attempts FEC by decoding the error correction code. If, after error detection, there are still errors present, the receiver will request a retransmission of that packet. Otherwise, the receiver accepts the received vector.
It is beneficial for an ARQ or HARQ receiver to utilize data from multiple transmissions of a packet, because even packets that contain errors carry some amount of information about the transmitted packet. However, due to system complexity, and in particular decoder complexity, many practical schemes only use data from a small, fixed number of transmissions.
Decision feedback equalization (DFE) for a multiple-input multiple-output (MIMO) data transmission or storage system with hybrid automatic repeat request (HARQ) is provided.
A MIMO transmitter, which has NT outputs, may send an NT-dimensional signal vector to the receiver. The receiver, which has NR inputs, may receive an NR-dimensional signal vector corresponding the NT-dimensional transmit vector. Using a HARQ protocol the MIMO transmitter may send the same signal vector multiple times to the receiver. A DFE at the receiver may be used to recover the transmitted signal vector from the multiple received signal vectors.
In some embodiments, a pre-equalization combining DFE approach is used. In this approach, the receiver concatenates the received signal vectors into a combined received signal vector. Channel state information associated with each of the received signal vectors is also concatenated into combined channel state information. Decision feedback equalization is performed on the combined received signal vector using the combined channel state information. Cholesky factorization and QR decomposition may be used by the DFE to process the combined channel state information. An estimated transmitted signal vector may be determined based on the equalized signal vector. The DFE may be performed based on the concatenated received signal vectors and the concatenated channel state information in their entireties. The DFE may also be performed incrementally by combining each received signal vector and channel state information with channel and signal information from previous transmissions.
In some embodiments, a post-equalization combining DFE approach is used. In this approach, the receiver performs decision feedback equalization on each of the received signal vectors using the channel state information associated with each of the received signal vectors. After the signal vectors are equalized, they may be combined into a combined equalized signal vector. An estimated transmitted signal vector may be determined based on the combined equalized signal vector. Cholesky factorization and QR decomposition may be used by the DFE to process the combined channel state information.
The above and other advantages of the invention will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which:
Decision feedback equalization (DFE) for multiple-input multiple-output (MIMO) data transmission or storage system with hybrid automatic repeat request (HARQ) is provided.
In the following, (•)T denotes transpose, whereas (•)* denotes conjugate transpose. x denotes a column vector. A denotes a matrix. Depending on the context, 0 denotes either a zero vector or a matrix with all elements equal to zero.
In one embodiment,
Returning to
One embodiment of transmitter 102 is shown in
Modulators 304 group the incoming bits into symbols, which are mapped and converted to signals according to a signal constellation set and carrier signal. In one embodiment, modulator 304 uses quadrature amplitude modulation (QAM). Each symbol is mapped to a signal point in the QAM signal constellation set, where the signal points are differentiated from one another by phase and/or magnitude.
Even though x is transmitted, receiver 112 in
yn=Hnx+zn1≦n≦K (1)
For clarity,
Noise may be modeled as additive white Gaussian noise (AWGN) sources. These noise sources may be independent and identically distributed (i.i.d). That is, the noise that affects one of the NR components in zn does not affect the noise for any other component in zn. Also, all of the noise sources may have the same probabilistic characteristics. Furthermore, each component of zn may have zero mean and may be random in terms of both magnitude and phase, where the magnitude and the phase are also independent. A noise source that has all of these characteristics is called an i.i.d. zero mean circularly symmetric complex Gaussian (ZMCSCG) noise source. If the covariance of noise matrix zn is σz2INR, then the conditional probability distribution function (pdf) of the received signal, ƒ(yn|Hn,x), is given by
Receiver 112 may use a decision feedback equalizer (DFE), as described in greater detail below, to determine the signal x that was transmitted based on the one or more received copies of yn. As shown in equation (1) the same signal x may be retransmitted n-times until x can be determined from the received signals yn. In some HARQ schemes, instead of retransmitting the same signal x, the transmitter may use precoding to form a different signal xn at each retransmission. In these schemes, the effect of the precoding can be included in channel matrix Hn for each of the n transmissions of signal x. Then, Hn is an effective channel matrix and the transmit signal x can be considered to be the information signal before preceding. Thus, equation (1) can also be used when precoding is used by the transmitter. Therefore it can be seen that the embodiments described herein may be used with HARQ schemes that use any suitable precoding techniques or with no precoding without affecting the overall operation of the system.
After the n-th transmission of signal x, all of the received signals y1 through yn can be combined into a concatenated signal vector yc,n. Thus equation (1) can be written as:
yc,n=Hc,nx+zc,n (3)
where,
yc,n=[y1T . . . ynT]T (4)
Hc,n=[H1T . . . HnT]T (5)
zc,n=[z1T . . . znT]T. (6)
Concatenated received signal yc,n and concatenated noise zc,n are n·NR×1 vectors while Hc,n is an n·NR×NT concatenated channel state matrix. For clarity,
To simplify the presentation, and in order to focus on the overall architecture, the equalizer design is explored with the zero-forcing (ZF) DFE. The zero-forcing equalizer may be followed by a simple, linear decoder. However, it should be understood that ZF is just one equalization technique that may be used. The present invention, however, is not limited to any particular type of signal processing or decoding. For example, a minimum mean squared error (MMSE) equalizer/decoder may also be used.
DFE 600 receives concatenated received signal yc,n and concatenated channel state matrix Hc,n. As can be seen in the figure, the first operation that is performed on the received signal is matched filtering:
Matched filtering is performed by correlating a known signal with an unknown signal to detect the presence of the known signal in the unknown signal. Here the conjugate transpose of concatenated channel state matrix Hc,n is correlated with the concatenated received signal yc,n to detect the presence of Hc,nx within yc,n. An estimated value of transmitted signal x can be generated by equalizing matched-filtered signal vn by a feedforward filter Fn and a feedback filter Bn.
The feedforward filter Fn and a feedback filter Bn may be calculated using a Cholesky factorization of the equivalent channel state matrix Sc,n after matched filtering:
Sc,n=Hc,n*Hc,n. (8)
Let the Cholesky factorization of Sc,n produce
Sc,n=Gc,n*Γc,nGc,n. (9)
where Γc,n is diagonal with positive elements and Gc,n is upper triangular and monic. Then the feedforward filter and the feedback filter are equal to
Fn=Γc,n−1Gc,n−* (10)
and
Bn=I−Gc,n (11)
respectively. Applying these filters as shown in
and
Finally, at decision block 610 an estimated value of transmitted signal x, {circumflex over (x)}, may be generated based on Gc,n and the estimated value of transmitted signal from n−1-th transmission. Decision block may be a simple, linear decoder or any other suitable decoder.
Referring to equations (4), (5), and (7), it can be seen that the output of the matched filter can be calculated incrementally as follows:
vn=vn−1+Hn*yn (14)
with v0=0. Similarly, from equations (5) and (8), the equivalent channel can be calculated incrementally using
Sc,n=Sc,n−1+Hn*Hn (15)
with S0=0. This incremental calculation of the matched filter output vn and the equivalent channel Sn has the benefit of reducing the required storage space at the receiver. Compared to the approach of equation (7) where vn is calculated directly from yc,n which is a n·NR×1 vector, the incremental calculation of equation (14) only used vectors having NT elements. Further only
complex numbers need to be stored for incremental channel estimate Hn, compared to nNRNT, complex numbers for concatenated channel state matrix Hc,n. The reason why it may not be necessary to store a full NT×NT matrix is because Sc,n=Σi=1nHi*Hi is Hermitian. DFE 700 using the incremental processing of equations (14) and (15) is shown in
DFE 600 and DFE 700 as shown in
Hi*Hi=Gi*ΓiGi. (16)
If there are no errors in the detection of received signal yi, an equalized signal ui may be represented by
ui=x+zi′, (17)
where zi′=Γi−1Gi−*Hi*zi and E[zi′zi′*]=σ22Γi−1. Thus, if the energy of each component xi,k of xi is normalized to 1, the SNR of the k-th stream for the i-th transmission may be
where γi,k,k is the (k, k)-th element of the diagonal matrix Γi. Maximal ratio combining for the k-th stream may consist of the operation
where ui,k is the k-th element of the equalizer output ui. Therefore, the SNR of the k-th stream after combining may be equal to
In this manner, maximal ratio combining (MRC) may maximize the SNR of each stream k to increase the ability to estimate the transmitted signal x from the received signal vectors yi.
The post-equalization combining scheme described above forms each vector ui based on the received signal vector yi of the i-th transmission of signal vector x. After being equalized, all of the soft estimates ui may be combined using MRC before being sent to the slicer that produces the final estimate {circumflex over (x)}.
The performance of this post-equalization combining DFE may be improved by maximal ratio combining of information from all transmissions 1 to i when forming ui. Then, the decision is based directly on ui rather than the a posteriori maximal ratio combination of the ui's. This “layered” approach is described using a simple example where n=2 transmissions and NT=2. For this example, ai(k) denotes the k-th element of a transmitted signal vector ai. For the first transmission, for both post-equalization combining schemes, u1(2)=t1(2), and {circumflex over (x)}1(2) results from slicing t1(2). Then, u1(1)=t1(1)+b1{circumflex over (x)}1(2), where b1 is element (1,2) of the 2×2 channel matrix B1. {circumflex over (x)}1(1) may be obtained by slicing ui(1).
For the second transmission, the first, a posteriori (non-layered) post-equalization combining method obtains u2(2) and u2(1) in the same manner, i.e., u2(2)=t2(2), and u2(1)=t2(l)+b2└u2(2)┘ (where └•┘ denotes the slicing operation). The soft estimates u1 and u2 may then be combined using MRC to produce {circumflex over (x)}2.
For the “layered” scheme, the soft estimate u2′ corresponding to the second transmission is formed by maximal ratio combining of all soft estimates. Therefore,
and
Similarly,
Although the expression for {circumflex over (x)}2 is similar for both schemes, the value by which γ2,1,1 is multiplied is different. For the first scheme it is just the sliced value of t2(2), whereas for the second scheme, it is the sliced value of the maximal ratio combination of the soft estimates of x(2) for each transmission as expressed by equation (25). {circumflex over (x)}2(2) is equal to {circumflex over (x)}′2(2), but this will only be true for the NT-th element in the general case. As the SNR of the quantity that is used for the calculation of {circumflex over (x)}′2(2) is not smaller than the SNR of t2(2), {circumflex over (x)}′2(2) will be at least as reliable as └t2(2)┘. Thus, it can be expected that, in general, the performance of the second, “layered” post-equalization combining scheme will more accurate that the posteriori, “non-layered” post-equalization combining method. An illustrative block diagram of a post-equalization “layered” DFE 900 is shown in
Although the “layered” post-equalization combining scheme may be slightly more complex to implement, the memory requirements do not increase. After weighting the soft estimate by γi,k,k and updating the sum, γi,k,k can be discarded. Therefore after processing each incoming vector yi only the soft estimates ui and the diagonal matrix {tilde over (Γ)}i need to be stored.
Consider again the concatenated channel model of equation (3). QR decomposition of the concatenated channel matrix Hc,n produces
Hc,n=Qc,nRc,n, (27)
where Qc,n is a unitary matrix and Rc,n is an upper triangular matrix with diagonal elements that are real and positive. The constraint that the diagonal elements are real and positive is not essential. However, with this constraint, the uniqueness of QR decomposition holds, and the uniqueness property is used later. The first operation performed on the combined received signal yc,n is a projection onto the vector space spanned by the columns of Qc,n:
wn=Qc,n*yc,n. (28)
The equivalent system model becomes
wn=Ec,nx+Qc,n*zc,n=Rc,nx+{tilde over (z)}c,n, (29)
with {tilde over (z)}c,nE[{tilde over (z)}c,n{tilde over (z)}c,n*]=σz2IN
Again, wn can be equalized with a combination of a feedforward and a feedback filter. The feedforward filter can be
Kn=[diag(Rc,n)]−1, (30)
where diag(A) denotes a diagonal matrix whose elements are equal to the diagonal elements of A. The feedback filter can be
Bn=I−KnRc,n. (31)
It can be seen that Bn is upper triangular matrix with zero diagonal elements and therefore a valid DFE feedback filter.
Similar to the DFE based on Cholesky factorization, the QR-based DFE can operate incrementally.
H1=Q1R1 (32)
and
{tilde over (w)}1=Q1*y1. (33)
For the processing of the second transmission, the receiver may only store {tilde over (w)}1 and the equivalent channel {tilde over (R)}1=R1. After the n-th transmission, the following relation is satisfied
where
and the QR decomposition of {tilde over (H)}n is given by
{tilde over (H)}n={tilde over (Q)}n{tilde over (E)}n. (36)
Then the sufficient statistic {tilde over (w)}n is equal to
The feedforward and the feedback filter after each transmission n can be calculated based on {tilde over (R)}n. Using the fact that QR decomposition is unique by constraining the diagonal elements of the R matrix to be real and positive, it can be seen that {tilde over (R)}n=Rc,n and {tilde over (w)}n=wn. Therefore, the performance of the DFE 1100 with incremental processing may be the same as the performance of the DFE 1000.
Similar to the Cholesky-based incremental DFE 700, only a triangular matrix {tilde over (R)}n and a vector {tilde over (w)}n need to be stored after each iteration. However, QR-based incremental DFE 1100 may be preferable to Cholesky-based incremental DFE 700 because QR decomposition may be calculated quite efficiently using Givens rotation because of the zero elements in {tilde over (R)}n. An MMSE-DFE may be designed in a similar fashion for DFE 1000 or DFE 1100, using the QR decomposition of the concatenated channel. As in the case of Cholesky Factorization, QR decomposition may be performed on the augmented matrix
where
is the SNR (equal for all elements of x).
A QR decomposition post-equalization combining scheme may be similar to the post-equalization combining scheme based on Cholesky factorization. The only difference between these to DFE schemes is the way that the MRC weights are calculated, as the MRC weights need to be expressed in terms of the elements of [diag(Ri)]2 instead of Ti
Assuming, as in the case of Cholesky factorization, that there is no error in the detection, the equalized signal ui can be represented as
ui=x+zi′, (38)
where zi′=[diag(Ri)]−1Qz*zi and E[zi′zi′*]=σz2[diag(Ri)]−2. It can be seen that equation (38) is the same as equation (17). Thus, QR-based post-equalization combining architectures may be provided using the same architecture as the Cholesky-based approach with γi,k,k replaced by r2i,k,k where ri,k,k is the (k,k)-th element of Ri.
In some embodiments, the order of equalization may effect the performance of the DFE. Let H† denote the Moore-Penrose pseudo-inverse of a matrix H (non-square, in general): H†=(H*H)−1H*. For every value of k from 1 to NT, ik will be the row of the pseudo-inverse Hc,n† of Hc,n with the smallest norm, excluding rows {i1, . . . ik−1}. The ik-th column of Hc,n is then replaced by the all-zero vector. Then, this new Hc,n will be used for the next iteration, k+1. With this ordering, the decoding may done in the order of i1, i2 . . . iN
This ordering algorithm may not be implemented, however, with incremental pre-equalization combining Cholesky factorization DFE 700. The ordering algorithm involves the calculation of the pseudo-inverse, which requires access to the concatenated channel matrix Hc,n. However, as previously described, incremental DFE 700 stores the Hermitian, NT×NT matrix Sc,n=Hc,n*Hc,n and the NT-dimensional vector vn=Hc,n*yc,n. It may be desirable within this embodiment to use the ordering algorithm while avoiding the necessity to store the (generally large) n·NR×NT matrix Hc,n. This can be accomplished by noting that the ordering can be based on Sc,n=Hc,n*Hc,n. In fact Hc,n†(Hc,n*Hc,n)−1Hc,n*Hc,n(Hc,n*Hc,n)−*=(Hc,n*Hc,n)−1=Sc,n−1. The diagonal element of {tilde over (s)}i,i of Sc,n−1, is therefore equal to the squared norm of the i-th row of Hc,n† that is used by the original ordering algorithm. As can also be seen, replacing the i-th column of Hc,n, by the all-zero vector results in Sc,n, with zeros along the i-th row and the i-th column. Hence, for incremental pre-equalization combining Cholesky-based DFE 700, the following ordering procedure can be used. For every value of k from 1 to NT, (ik, ik) will be the index of the smallest diagonal element of Sc,n−1, excluding the diagonal elements (ik, ik), . . . , (ik−1, ik−1). All elements of the ik-th column and the ik-th row of Sn are replaced by zeros. Then, this new Sn will be used for the next iteration, k+1.
When a post-equalization combining Cholesky factorization DFE is used, the ordering can be done independently for each transmission in a straightforward way. The following ordering algorithm may be implemented where the combining is done after the feedforward filter but before the feedback filter. Let the permutation matrix that describes the ordering after (n−1)-th transmission be Pn−1. Then
Pn−1T{tilde over (H)}n−1*{tilde over (H)}n−1Pn−1={tilde over (G)}n−1*{tilde over (Γ)}n−1{tilde over (G)}n−1. (39)
An equivalent signal model for the transmission up to (n−1) is
{tilde over (Γ)}n−11/2un−1={tilde over (Γ)}n−11/2Pn−1Tx+{tilde over (z)}n−1, (40)
where E[{tilde over (z)}n−1{tilde over (z)}n−1*]=σz2IN
where the equivalent channel matrix equals
After each transmission n the ordering can be done based on the equivalent channel matrix {tilde over (H)}n. If the receiver requests a retransmission after having received the n-th signal, the vector {tilde over (Γ)}n1/2un and the matrix {tilde over (Γ)}n1/2OnT are stored for the next iteration. As described previously, the computation of un depends on which of the two post-equalization combining schemes is used.
Two exemplary ordering algorithms may be used in connected with pre-equalization combining QR-decomposition DFE 1100, optimal ordering and low-complexity sorted QR ordering. These exemplary ordering algorithms are described in B. Hassibi, “An efficient square-root algorithm for BLAST,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Istanbul, Turkey, Jun. 5-9, 2000, pp. 737-740 and D. Wübben, R. Böhnke, V. Kühn, and K. D. Kammeyer, “MMSE extension of V-BLAST based on sorted QR decomposition,” in Proc. IEEE Vehicular Technology Conference (VTC) 2003—Fall, Orlando, Fla., October 2003, pp. 508-512, respectively. Both of these references are incorporated herein in their entirety. For incremental pre-equalization combining QR-decomposition DFE 1100, a different ordering procedure is used. As shown in (29),
w=Rx+Q*z=Rx+{tilde over (z)}, (43)
where H=QR. Let the permutation matrix that describes the ordering after the (n−1)-th transmission be Pn−1 and
{tilde over (H)}n−1Pn−1={tilde over (Q)}n−1{tilde over (R)}n−1. (44)
After the (n−1)-th transmission,
wn−1=Rn−1Pn−1Tx=Qn−1*zn−1={tilde over (R)}n−1x+{tilde over (z)}n−1. Therefore, the equivalent signal model after the n-th transmission can be expressed as
where E[{tilde over (z)}n−1{tilde over (z)}n−1*]=σz2IN
From the above it can be seen that after each transmission vector wn and the matrix RnPnT should be stored after each iteration. After each transmission, any suitable ordering algorithm for the QR approach can be applied.
When a post-equalization combining QR-decomposition DFE is used, similar to the steps leading to equation (38), assuming that there was no error in the detection,
ui=PTx+zi′, (47)
where zi′=[diag({tilde over (R)}i)]−1Qi*zi and E[zi′zi′*]=σz2I[diag({tilde over (R)}i)]−2. Thus, the equivalent signal model after the n-th transmission becomes
where E[{tilde over (z)}n−1{tilde over (z)}n−1*]=σz2I, and the equivalent channel matrix {tilde over (H)}n is equal to
Based on this equivalent channel matrix, new ordering using any suitable ordering algorithm may be applied. Thus, for an incremental post-equalization combining QR-decomposition approach, if a retransmission is requested, diag({tilde over (R)}n−1)Pn−1T and un−1 is stored after the current transmission.
Alternatively, the ordering can be done without using the large equivalent channel matrix {tilde over (H)}n with a slight modification in the norm calculation process of the ordering algorithms because of the fact that there is only one nonzero element for every row of diag({tilde over (R)}n−1)Pn−1T.
Referring now to
Referring now to
The HDD 1400 may communicate with a host device (not shown) such as a computer, mobile computing devices such as personal digital assistants, cellular phones, media or MP3 players and the like, and/or other devices via one or more wired or wireless communication links 1408. The HDD 1400 may be connected to memory 1409 such as random access memory (RAM), low latency nonvolatile memory such as flash memory, read only memory (ROM) and/or other suitable electronic data storage.
Referring now to
The DVD drive 1410 may communicate with an output device (not shown) such as a computer, television or other device via one or more wired or wireless communication links 1417. The DVD drive 1410 may communicate with mass data storage 1418 that stores data in a nonvolatile manner. The mass data storage 1418 may include a hard disk drive (HDD). The HDD may have the configuration shown in
Referring now to
The HDTV 1420 may communicate with mass data storage 1427 that stores data in a nonvolatile manner such as optical and/or magnetic storage devices for example hard disk drives and/or DVD drives. At least one HDD may have the configuration shown in
Referring now to
The present invention may also be implemented in other control systems 1440 of the vehicle 1430. The control system 1440 may likewise receive signals from input sensors 1442 and/or output control signals to one or more output devices 1444. In some implementations, the control system 1440 may be part of an anti-lock braking system (ABS), a navigation system, a telematics system, a vehicle telematics system, a lane departure system, an adaptive cruise control system, a vehicle entertainment system such as a stereo, DVD, compact disc and the like. Still other implementations are contemplated.
The powertrain control system 1432 may communicate with mass data storage 1446 that stores data in a nonvolatile manner. The mass data storage 1446 may include optical and/or magnetic storage devices for example hard disk drives and/or DVD drives. At least one HDD may have the configuration shown in
Referring now to
The cellular phone 1450 may communicate with mass data storage 1464 that stores data in a nonvolatile manner such as optical and/or magnetic storage devices for example hard disk drives and/or DVD drives. At least one HDD may have the configuration shown in
Referring now to
The set top box 1480 may communicate with mass data storage 1490 that stores data in a nonvolatile manner. The mass data storage 1490 may include optical and/or magnetic storage devices for example hard disk drives and/or DVD drives. At least one HDD may have the configuration shown in
Referring now to
The media player 1500 may communicate with mass data storage 1510 that stores data such as compressed audio and/or video content in a nonvolatile manner. In some implementations, the compressed audio files include files that are compliant with MP3 format or other suitable compressed audio and/or video formats. The mass data storage may include optical and/or magnetic storage devices for example hard disk drives and/or DVD drives. At least one HDD may have the configuration shown in
The above described embodiments of the present invention are presented for the purposes of illustration and not of limitation. Since many embodiments of the invention can be made without departing from the scope of the invention, the invention resides in the claims hereinafter appended. Furthermore, the present invention is not limited to a particular implementation. The invention may be implemented in hardware, such as on an application specific integrated circuit (ASIC) or on a field-programmable gate array (FPGA). The invention may also be implemented in software. In addition, one or more methods of steps discussed above can be performed in a different order or concurrently to achieve desirable results.
This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 60/911,151, filed Apr. 11, 2007, which is incorporated herein by reference in its entirety.
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