23-09-2016, 12:00 PM
1455816590-JOURNALPAPER1.docx (Size: 663.13 KB / Downloads: 5)
ABSTRACT---Multiple-input multiple-output (MIMO) single carrier frequency division multiple access (SC-FDMA), which combines the advantage of diversities with frequency domain equalizers (FDE), has drawn great attention recently. Due to invention of digital video broadcasting 16-QAM and 64-QAM schemes are most widely used in recent wireless systems. In this paper we introduce a novel low-complexity multiple-input multiple-output (MIMO) detector tailored for single-carrier frequency division-multiple access (SC-FDMA) systems, suitable for efficient hardware implementations. The proposed detector starts with an initial estimate of the transmitted signal based on a minimum mean square error (MMSE) detector. Then in order to improve the initial estimate rate less reliable symbols with more candidates in the constellation are soft decoded. Efficient high-throughput VLSI architecture is used to achieve superior performance compared to the conventional MMSE detectors. The efficiency of MIMO over high order constellations are verified through MATLAB BER simulation and complexity reduction is proved with hardware synthesis results.
1. INTRODUCTION
THE 3rd generation partnership project (3GPP) defined long term evolution (LTE) to meet the requirements of the 4G wireless communication. LTE combines multiple-input multiple-output (MIMO) technology with orthogonal frequency division-multiple access (OFDMA) technology in the downlink and single carrier-frequency division multiple access (SC-FDMA) in the uplink to achieve peak data rates of 300 Mbps and 75 Mbps, respectively.
The SC-FDMA utilizes a discrete Fourier transform-spread OFDM (DFT-S-OFDM) modulation with similar performance compared to the OFDM. Its main advantage is to provide a lower peak-to-average power ratio (PAPR), which makes it the technology of the choice for the uplink . However, the implementation of a MIMO detector in an SC-FDMA system is significantly more complicated than that of an OFDMA system. This is due to the fact that the transmitted data is mixed together because of the extra DFT block used naturally in an SC-FDAM system. Therefore, the implementation of a low-complexity MIMO detector is needed and is the main challenge in the SC-FDMA framework.
Several designs have been proposed for SC-FDMA MIMO detectors among which the linear frequency domain equalizer (FDE) receivers, including the minimum mean square error (MMSE) and zero forcing (ZF), are often used due to their simplicity . Considering the compromise between the BER performance and the complexity, typically suboptimal methods are employed. In this paper, a detection scheme is proposed for MIMO SC-FDMA systems, which near-optimal performance with a significant reduction in the complexity especially for large constellation sizes.
2. SYSTEM MODEL
A.Transmitter
Fig. 1 shows the transmitter side of a MIMO SC-DAM system with MR transmit and MR receiver antennae supporting users. The data stream on each transmit antenna is groupedinto blocks of symbols, as follows
sn(t)(k)=[sn(t)(k)(0),sn(t)(k)(1),….,sn(t)(k)(m-1)]T, (1)
where the superscript represents the transpose operation, nt is the antenna index, M is the DFT size, and Sn(t)(k) represents the data on the transmit antenna n(t) for user K , whose elements are chosen from a-ary quadrature amplitude modulation (QAM) constellation. After the DFT operation, the frequency domain(FD) representation of data on antenna n(t) is obtained and is denoted by sn(t)(k) .
The next step in the SC-FDMA transmitter is to map the M frequency domain outputs of the DFT block to N existing orthogonal sub-carriers, denoted by the “Sub-carrier mapping” in Fig. 1. There are two typical methods for the sub-carrier allocation,i.e., the localized and distributed method.
3. HARD DECISION DETECTION
The PDP algorithm in this paper, consisting of three stages, is illustrated in Fig.3.1, where, is the channel matrix for the sub-carrier and the superscript is the Hermitian transform. These stages are described in the sequel.
First Stage: An MMSE equalizer1 is utilized to produce the initial estimate of the symbol sequence by reversing the channel effect for each sub-carrier to estimate the transmitted FD signals. Subsequently, an –point IDFT operation is executed on all sub-carriers to find time-domain signals. Therefore, the effect of the channel and the DFT are taken into account independently in this stage of the detection process. The IDFT outputs (i.e.,) are then mapped to the constellation points and grouped to produce symbols in the initial estimate.
4. SOFT DETECTION SCHEME
The architecture provides a hard decision output (i.e., ) based on the transmitted symbols. While the proposed structure provides a superior BER performance compared to the conventional MMSE receivers, a soft-coded system is proposed that complies with advanced wireless standards. In a coded system, the transmitter encodes the message by using an error-correcting code. At the receiver, the decoding is performed based on the extrinsic log-likelihood ratios (LLR) calculated by the MIMO detector. The LLRs are in fact the soft information representing the reliability of the detection. In contrast to a hard MIMO detector where a hard decision is made for each bit, a soft MIMO detector generates a value for each bit representing the probability of its being one or zero.
In order to enhance the performance of the coded system, the MIMO detector will have to generate a soft decision based on the transmitted symbols. In a-ary QAM modulation, LLR values must be calculated for all bits in each symbol resulting in log2 Q×P of LLR calculations for symbols.
6. CONCLUSIONS
In this paper, we analyze the performance of different equalization algorithm for SCFDMA system. Initially we analyzes the MIMO sytems and SCFDMA multiple access scheme with various ,modulation schemes over distance metrics .The proposed algorithm is based on channel estimation that exploits the sparsity of the estimated error signal. We also perform MMSE with soft decoding based symbol selection in each iteration to prove the fast convergence. We illustrated the performance of our algorithm in numerical simulations, and our algorithm shows a significant performance improvement compared to linear equalizers, while the BER rate is much lower compared to feeding back one symbol at a time for channel estimation.