An Efficient Joint Data Detection and Channel Estimation Technique for Uplink MC-CDMA Systems Based on SAGE Algorithm

Abstract

This paper is concerned with joint channel estimation and data detection for uplink multicarrier code-division multiple-access (MC-CDMA) systems in the presence of frequency fading channel. The detection and estimation algorithm, implemented at the receiver, is based on a version of the expectation maximization (EM) technique, called the spece-alternating-generalized-expectation-maximization (SAGE) algorithm which is very suitable for the the multicarrier signal formats. Application of the SAGE algorithm to the problem of iterative data detection and channel estimation leads to a receiver structure that also incorporates a partial interference cancelation. Computer simulations show that the proposed algorithm has excellent BER end estimation performance.Index Terms: Joint data detection and channel estimation, MC-CDMA Systems, SAGE algorithm. I. INTRODUCTIONIn this paper we consider an efficient iterative algorithm based on the SAGE technique for multi-user data detection and channel estimation, jointly for uplink MC-CDMA systems in the presence of frequency selective fading channels. The SAGE algorithm is a broadly applicable approach to the iterative computation of parameters from intractable and high complexity likelihood functions. An EM approach proposed for the superimposed signals [1] is applied to the channel estimation for OFDM systems [2], [3] and compared with SAGE version in [4]. As will be seen shortly, a partial parallel interference cancelation (PIC) is incorporated into the resulting detection algorithm [5], [6]. The work is an extension of [7] in which joint data detection and channel estimation of uplink DS-CDMA systems were considered in the presence of flat Rayleigh channels. We extend their results for the uplink MC-CDMA systems with frequency selective channels. The channel estimation becomes more challenging for uplink systems since each channel between every user and the base station must be estimated rather than estimating a single channel in case of a downlink transmission.Notation: Vectors (matrices) are denoted by boldface lower (upper) case letters; all vectors are column vectors; (.) * , (.) T and (.) H denote the conjugate, transpose and conjugate transpose, respectively; . denotes the Frobenius norm; I L denotes the L × L identity matrix; diag{.} denotes a diagonal matrix; and finally, tr{.} denotes the trace of a matrix.

Iterative Receiver with Low Complexity for Downlink Multicarrier Communications over Rapidly Time‐Varying Channels

“…The adverse effects of MAI can be further reduced using estimates of all active users' data and channel coefficients. With this motivation, researchers studied iterative parallel interference canceller-(PIC-) based multiuser data detection techniques for CDMA-based systems to enhance their performance [6][7][8][9][10][11]. That is why maximum likelihood (ML) based algorithms such as expectation-maximization (EM) and spacealternating generalized expectation-maximization (SAGE) are preferred [11][12][13][14].…”

“…Although ML type algorithms are able to collectively estimate data and channel coefficients, they have computational complexity and slowness problem. To 2 Wireless Communications and Mobile Computing this end, these algorithms were mostly usedin the uplink communication of multicarrier systems in [9][10][11][12] and rarely in downlink communication systems for data detection, channel estimation, beamforming, and direction of arrival estimation with a cost of high computational complexity [14][15][16][17][18][19]. Despite high computational complexity, some of these studies show that jointly estimate of channel coefficients and user's data is a good alternative for the downlink communication of MC-CDMA system to improve the performance.…”