The enormous growth of consumer electronic industry is driving the need for achieving ultra-high areal storage densities. Shingled magnetic recording (SMR) is a promising high density storage technique where shingles are created by overlapping tracks for increasing the track per inch (TPI) and achieving higher areal densities. High TPI values, however, introduce sidetrack interference in the equalized output of the center track sensed through the read head. The problem of removing interference from the center track signal can be formulated as estimating pulse shapes of sidetracks assuming sidetracks are decodable. In this paper, we first propose a solution using the correlation method for estimating the pulse shape and the phase-misalignment. The correlation method fails when there is a frequency offset present from sidetracks. Furthermore, we devise a solution based on the least mean square (LMS) algorithm which can provide time varying estimates of the sidetrack pulse shapes and remove the interference from the center track signal. Experimental and simulation results which corroborate the analysis of the proposed algorithm, demonstrate that the proposed algorithm outperforms other techniques and gives the minimum bit error rate (BER). The complexity of the proposed algorithm is low and it can be easily implemented on the hardware.

In this paper, we present an algebraic methodology for implementing low-complexity, Chase-type, decoding of Reed–Solomon (RS) codes of length$n$. In such, a set of$2^\eta$test-vectors that are equivalent on all except$\eta \ll n$coordinate positions is first produced. The similarity of the test-vectors is utilized to reduce the complexity of interpolation, the process of constructing a set of polynomials that obey constraints imposed by each test-vector. By first considering the equivalent indices, a polynomial common to all test-vectors is constructed. The required set of polynomials is then produced by interpolating the final$\eta$dissimilar indices utilizing a binary-tree structure. In the second decoding step (factorization) a candidate message is extracted from each interpolation polynomial such that one may be chosen as the decoded message. Although an expression for the direct evaluation of each candidate message is provided, carrying out this computation for each polynomial is extremely complex. Thus, a novel, reduced-complexity, methodology is also given. Although suboptimal, simulation results affirm that the loss in performance incurred by this procedure is decreasing with increasing code length$n$, and negligible for long$(n > 100)$codes. Significant coding gains are shown to be achievable over traditional hard-in hard-out decoding procedures (e.g., Berlekamp–Massey) at an equivalent (and, in some cases, lower) computational complexity. Furthermore, these gains are shown to be similar to the recently proposed soft-in-hard-out algebraic techniques (e.g., Sudan, Kötter–Vardy) that bear significantly more complex implementations than the proposed algorithm.

In this paper, we present a practical approach to the iterative decoding of Reed-Solomon (RS) codes. The presented methodology utilizes an architecture in which the output produced by steps of belief-propagation (BP) is successively applied to a legacy decoding algorithm. Due to the suboptimal performance of BP conducted on the inherently dense RS parity-check matrix, a method is first provided for the construction of reduced-density, binary, parity-check equations. Iterative decoding is then conducted utilizing a subset of a redundant set of parity-check equations to minimize the number of connections into the least-reliable bits. Simulation results show that performance comparable to (and exceeding) the best known practical RS decoding techniques is achievable with the presented methodology. The complexity of the proposed algorithm is significantly lower than these existing procedures and permits a practical implementation in hardware.

In this work we present a low-complexity implementation of Chase-type decoding of Reed-Solomon Codes. In such, we first use the soft-information available at the channel output to construct a test-set of 2ηvectors, equival ent in all except the ηηbivariate polynomials, with the roots of each specified by its corresponding test-vector. Here, test-vector similarity is exploited to share much of the required computation. Finally, we obtain the candidate message from the single z-linear factor of each bivariate polynomial. Although we provide an expression for the direct computation of each candidate message, the complexity of repeating this computation for each interpolation polynomial is prohibitive. We, thus, also present a reduced-complexity factorization (RCF) method to select a single polynomial that, with high probability, contains the correctly decoded message in its z-linear factor. Although suboptimal, the loss in performance of RCF decreases rapidly with increasing code length. We provide extensive simulation results showing that a significant performance increase over traditional hard-decision decoding is achievable with a comparable computational complexity (as implemented with the Berlekamp-Massey Algorithm).

In this study we consider the challenge of reliable communication over a wireless Rayleigh flat-fading channel using multiple transmit and receive antennas. Since modern digital communication systems employ signal sets of finite cardinality, we examine the use of the quadrature amplitude modulation (QAM) constellation to approach the capacity of this channel. By restricting the channel input to the M-QAM subset of the complex-plane, the maximum achievable information rate (C/sub M-QAM/) is strictly bounded away from the channel capacity (C). We utilize a modified version of the Arimoto-Blahut algorithm to determine C/sub M-QAM/ and the probability distribution over the channel input symbols that achieves it. The results of this optimization procedure numerically indicate that the optimal input symbol distribution factors into the product of identical distributions over each real dimension of the transmitted signal. This is shown to vastly reduce the computational complexity of the optimization algorithm. Furthermore, we utilize the computed optimal channel input probability mass function (pmf) to construct capacity approaching trellis codes. These codes are implemented independent across all antennas and symbol dimensions and, if used as inner codes to outer low-density parity check (LDPC) codes, can achieve arbitrarily small error rates at signal-to-noise ratios very close to the channel capacity C/sub M-QAM/. Examples are given for a 2-transmit/2-receive antenna (2 /spl times/ 2) system.

In this study we provide a physical layer based analysis of the coexistence issues of ultra-wideband (UWB) with other devices in the same spectrum. Specifically, we have focused on the UWB interference to and from devices using the wireless local area network (WLAN) standard IEEE 802.11a. Our results indicate that a UWB interferer operating at the peak allowable power density induces minimal interference into such WLAN devices in line-of sight (LOS) scenarios, even at close range. However, in the non-LOS (NLOS) case, a UWB interferer can severely affect the data-rate sustainable by 802.11a systems. Moreover, 802.11a interference into UWB systems is shown to reduce the signal-to-interference ratio (SIR) by as much as 36 dB when the interferer is within LOS of the UWB receiver.