ISSN (Online): 2320-9364, ISSN (Print): 2320-9356
www.ijres.org Volume 4 Issue 10 ǁ October. 2016 ǁ PP. 40-48
An Improved Ordered-Block MMSE Detector for Generalized
Spatial Modulation
P.Betsi
1, A. Pavani
2, E.V. Krishna Rao
3, B. Prabhakar Rao
41
(Department of ECE, Priyadarshini College of Engineering, Nellore, Andhra Pradesh, India) 2(Department of ECE, Priyadarshini College of Engineering, Andhra Pradesh, India)
3(Department of ECE, Lakireddy Bali Reddy College of Engineering, India) 4(Rector, JNTU Kakinada, Kakinada, Andhra Pradesh, India)
Abstract:
New trends are more essential in the area of wireless communication for different applications. Wireless communication is one of the important aspects of life. The growth of wireless communication technology has demand for the high speed, efficient, reliable voice and data communication. In future wireless networks will face a problem on supporting large traffic volumes for delay-sensitive traffic. Spatial modulation (SM) is a transmission technique proposed for multiple–input multiple-output (MIMO) systems, where only one transmit antenna is active at a time, offering an increase in the spectral efficiency equal to the base–two logarithm of the number of transmit antennas. In this Project An improved ordered-block minimum mean squared-error (OB-MMSE) detector for generalized spatial modulation systems is presented. We first propose to use the concentrated distance metric derived from the conditional maximum likelihood estimator as the ordering metric for the OBMMSE and then design a computationally-efficient algorithm for computing this metric. The improved ordering performance of the proposed algorithm allows the early-termination of the OB-MMSE detector without noticeable performance loss which can be exploited to further reduce its complexity. Simulation results show that the proposed algorithm can achieve better performance-complexity tradeoff compared to the existing OBMMSE detector.Keywords:
Generalized Spatial Modulation (GSM), Multiple Input – Multiple Output (MIMO), Spatial Modulation(SM).I.
INTRODUCTION
Multiple–input multiple–output (MIMO) systems offer a significant increase in spectral efficiency in comparison to single antenna systems. An example is spatial multiplexing (SMX), which transmits simultaneously over all the transmit antennas. This method achieves a spectral efficiency that increases linearly with the number of transmit antennas. However, with the exponential increase in wireless data traffic, a large number of transmit antennas (large scale MIMO) should be used. Large scale MIMO systems offer a higher data rate and better average bit error ratio (ABER). However, this comes with the expense of an increase where SMX–maximum–likelihood (ML) optimum receiver searches across all possible combinations, and tries to resolve the inter–channel interference (ICI), caused by transmitting from all antennas simultaneously on the same frequency. The sphere decoder (SD) is proposed to reduce the complexity of the SMX–ML while retaining a near optimum performance. The SD reduces the complexity of the ML decoder by limiting the number of possible combinations. Only those combinations that lie within a sphere centered at the received signal are considered. However, even though MX–SD offers a large reduction in complexity compared to SMX–ML, it still has a high complexity as it does not avoid ICI .In SMX the number of radio frequency (RF) chains is equal to the number of transmit antennas. From, RF chains are circuits that do not follow Moore’s law in progressive improvement. Therefore, increasing the number of transmit antennas and consequently the number of RF chains increases significantly the cost of real system implementation. RF chains contain Power Amplifiers (PAs) which are responsible for 50–80% of the total power consumption in the transmitter. Therefore, increasing the number of RF chains results in a decrease in the energy efficiency.
II.
SYSTEM MODEL OF THE MIMO COMMUNICATION
Multiple-Input Multiple-Output (MIMO) technology is a wireless technology that uses multiple transmitters and receivers to transfer more data at the same time. MIMO technology takes advantage of a radio-wave phenomenon called multipath where transmitted information bounces off walls, ceilings, and other objects, reaching the receiving antenna multiple times via different angles and at slightly different times.
Figure-1: MIMO Technology uses multiple radios to transfer more data at the same time MIMO technology leverages multipath behavior by using multiple, “smart” transmitters and receivers with an added “spatial” dimension to dramatically increase performance and range [6]. MIMO allows multiple antennas to send and receive multiple spatial streams at the same time. MIMO makes antennas work smarter by enabling them to combine data streams arriving from different paths and at different times to effectively increase receiver signal-capturing power. Smart antennas use spatial diversity technology, which puts surplus antennas to good use. If there are more antennas than spatial streams, the additional antennas can add receiver diversity and increase range.
2.1 Mimo - Basics
As a result of the use of multiple antennas, MIMO wireless technology is able to considerably increase the capacity of a given channel. By increasing the number of receive and transmit antennas it is possible to linearly increase the throughput of the channel with every pair of antennas added to the system. This makes MIMO wireless technology one of the most important wireless techniques to be employed in recent years. As spectral bandwidth is becoming an ever more valuable commodity for radio communications systems, techniques are needed to use the available bandwidth more effectively. MIMO wireless technology is one of these techniques.
2.1.1 Mimo - Siso
The advantage of a SISO system is its simplicity. SISO requires no processing in terms of the various forms of diversity that may be used. However the SISO channel is limited in its performance as interference and fading will impact the system more than a MIMO system using some form of diversity. The throughput depends upon the channel bandwidth and the signal to noise ratio.
2.1.2 Mimo - Simo
Figure-3: Simo - Single Input Multiple Output
SIMO has the advantage that it is relatively easy to implement although it does have some disadvantages in that the processing is required in the receiver. The use of SIMO may be quite acceptable in many applications, but where the receiver is located in a mobile device such as a cellphone handset, the levels of processing may be limited by size, cost and battery drain. There are two forms of SIMO that can be used: • Switched diversity SIMO: This form of SIMO looks for the strongest signal and switches to that antenna. • Maximum ratio combining SIMO: This form of SIMO takes both signals and sums them to give the a combination. In this way, the signals from both antennas contribute to the overall signal.
2.1.3 Mimo - Miso
Figure-4: MISO - Multiple Input Single Output
The advantage of using MISO is that the multiple antennas and the redundancy coding / processing is moved from the receiver to the transmitter. In instances such as cellphone UEs, this can be a significant advantage in terms of space for the antennas and reducing the level of processing required in the receiver for the redundancy coding. This has a positive impact on size, cost and battery life as the lower level of processing requires less battery consumption.
2.1.4 Mimo
Figure-5: MIMO - Multiple Input Multiple Output
One of the core ideas behind MIMO wireless systems space-time signal processing in which time is complemented with the spatial dimension inherent in the use of multiple spatially distributed antennas, i.e. the
use of multiple antennas located at different points. Accordingly MIMO wireless systems can be viewed as a logical extension to the smart antennas that have been used for many years to improve wireless. It is found between a transmitter and a receiver; the signal can take many paths. Additionally by moving the antennas even a small distance the paths used will change. By using MIMO, these additional paths can be used to advantage. They can be used to provide additional robustness to the radio link by improving the signal to noise ratio, or by increasing the link data capacity.
The two main formats for MIMO are given below:
• Spatial diversity: Spatial diversity used in this narrower sense often refers to transmit and receive diversity. These two methodologies are used to provide improvements in the signal to noise ratio and they are characterized by improving the reliability of the system with respect to the various forms of fading.
• Spatial multiplexing: This form of MIMO is used to provide additional data capacity by utilizing the different paths to carry additional traffic, i.e. increasing the data throughput capability. One of the key advantages of MIMO spatial multiplexing is the fact that it is able to provide additional data capacity. MIMO spatial multiplexing achieves this by utilizing the multiple paths and effectively using them as additional "channels" to carry data. The maximum amount of data that can be carried by a radio channel is limited by the physical boundaries defined under Shannon's Law. Multiple-input, multiple-output (MIMO) antenna systems are used in modern wireless standards, including in IEEE 802.11n, 3GPP LTE, and mobile WiMAX systems. The technique supports enhanced data throughput even under conditions of interference, signal fading, and multipath. The demand for higher data rates over longer distances has been one of the primary motivations behind the development of MIMO orthogonal- frequency-division-multiplexing (OFDM) communications systems.Shannon's law defines the maximum rate at which error free data can be transmitted over a given bandwidth in the presence of noise. It is usually expressed in the form:
Capacity = BW log2(1 + SNR) ---- Eq. 1
Where C is the channel capacity in bits per second, BW is the bandwidth in Hertz, and SNR is Signal to Noise Ratio. The above Eq. shows, an increase in a channel's SNR results in marginal gains in channel throughput. As a result, the traditional way to achieve higher data rates is by increasing the signal bandwidth. Unfortunately, increasing the signal bandwidth of a communications channel by increasing the symbol rate of a modulated carrier increases its susceptibility to multipath fading. For wide bandwidth channels, one partial solution to solving the multipath challenge is to use a series of narrowband overlapping subcarriers. The maximum channel capacity of a MIMO system, the channel capacity can be estimated as a function of N spatial streams. A basic approximation of MIMO channel capacity is a function of spatial streams, bandwidth, and signal-to-noise ratio (SNR) and is shown in the following Eq.
Capacity = N BW log2 (1 + SNR) --- Eq. 2
Given the equation for MIMO channel capacity, it is possible to investigate the relationship between the number of spatial streams and the throughput of various implementations of SISO and MIMO configurations. As an example, the IEEE 802.11g specs prescribe that a wireless-local- area network (WLAN) channel uses a SISO configuration. With this standard, the maximum coded data rate of 54 Mb/s requires use of a 64-QAM modulation scheme and a code rate of 3/4. As a result, the uncoded bit rate is 72 Mb/s (4/3 x 54 Mb/s). With minimum transmitter error vector magnitude (EVM) at -25 dB, an SNR of 25 dB can be estimated as the requirement for a 64-state quadrature amplitude- modulation (64QAM) scheme. While EVM and SNR are not equivalent in all cases, we can assume that the magnitude error of a symbol will dominate the signal error as the SNR approaches its lower limit. The maximum data rate of IEEE 802.11g maps closely with the maximum channel capacity dictated by the Shannon- Hartley theorem. According to this theorem, a Gaussian channel with an SNR of 25 dB should produce an uncoded data rate of 94 Mb/s in a 20-MHz channel bandwidth. By contrast, Eq. 2 would suggest that a MIMO channel with four spatial streams should be capable of four times the capacity of the SISO channel. 20-MHz channel with a signal-to-noise ratio (SNR) of 25 dB and four spatial streams should have an uncoded bit rate of 4 x 94 Mb/s = 376 Mb/s. This estimation maps closely with the expected data rates of the draft IEEE 802.11n physical layer specs. IEEE 802.11n is designed to support MIMO configurations with as many as four spatial streams. At the highest data rate, bursts using a 64QAM modulation scheme with a 5/6 channel code rate produce a data rate of 288.9 Mb/s and an uncoded bit rate of 346.68 Mb/s. At the highest data rate, the IEEE 802.11n channel with four spatial streams produces a data rate that is comparable to the theoretical limit of 376 Mb/s. It can be observed that the bit rate of a 4 x 4 (four spatial stream) MIMO configuration exceeds that of the Shannon- Hartley limit at all data rates, making MIMO systems attractive for higher data throughput. While MIMO systems provide users with clear benefits at the application level, the design and test of MIMO devices is not without significant challenges.
MIMO systems form an essential part of LTE in order to achieve the ambitious requirements for throughput and spectral efficiency. MIMO refers to the use of multiple antennas at transmitter and receiver side. 3.1 Downlink MIMO
For the LTE downlink, a 2x2 configuration for MIMO is assumed as baseline configuration, i.e. 2 transmit antennas at the base station and 2 receive antennas at the terminal side. Configurations with 4 antennas are also being considered. Different MIMO modes are envisaged. It has to be differentiated between spatial multiplexing and transmit diversity, and it depends on the channel condition which scheme to select.
Figure-6 shows the principle of spatial multiplexing, exploiting the spatial dimension of the radio channel which allows to transmit the different data streams simultaneously.
Figure -6 : Spatial multiplexing
Each transmit antenna transmits a different data stream. Each receive antenna may receive the data streams from all transmit antennas [6]. The channel (for a specific delay) can thus be described by the following channel matrix H: As above figure. In this general description, Nt is the number of transmit antennas, Nr is the number of receive antennas, resulting in a 2x2 matrix for the baseline LTE scenario. The coefficients hij of this matrix are called channel coefficients from transmit antenna i to receive antenna j, thus describing all possible paths between transmitter and receiver side. The number of data streams that can be transmitted in parallel over the MIMO channel is given by min {Nt, Nr} and is limited by the rank of the matrix H. The transmission quality degrades significantly in case the singular values of matrix H are not sufficiently strong.
Figure-7: Pre-coding principle00000 3.2 Uplink MIMO
Uplink MIMO schemes for LTE will differ from downlink MIMO schemes to take into account terminal complexity issues. For the uplink, MU-MIMO can be used. Multiple user terminals may UE will have two transmit antennas but only one transmit chain and amplifier. A switch will then choose the antenna that provides the best channel to the eNodeB
The optimal detector for the aforementioned GSM-MIMO system is the ML detector, given by among all ordered subsets {Ij}Nj=1 and all symbol vectors s ∈ΩNp
. Clearly, a straightforward exhaustive search approach requires a tremendous complexity, and hence is inefficient for practical implementation. Recently, a low-complexity OBMMSE detector featuring near-ML performance has been proposed. The main idea of
OB-MMSE is to sort all the potential TACs according to an ordering criterion, and then detect the associated symbol vector using a conventional MMSE detector for each TAC. Let the MMSE detected result corresponding to Ijbe
denoted as ˆsIj. The distance metric d(Ij,ˆsIj) is computed and then compared with a predefined threshold vth. If
the distance metric is smaller than vth,the algorithm outputs the current results (j,ˆsIj) as the final decision and
terminates. Otherwise, the algorithm compares distance metrics in all the N hypotheses and outputs the detection results associated to the hypothesis that has the smallest d(Ij,ˆsIj).
Clearly, ordering plays an important role in OB-MMSE. A good ordering criterion places the true TAC in lower rank so that the true TAC is tested earlier, which results in early termination. Now, we elaborate on how the TACs are ordered in OB-MMSE.
We first consider the conditional ML estimate (MLE) for s, under the condition that HIiis given. From
the above equation, the CMLE ˜sIiis obtained as
˜sIi=H†Iiy = (HHIiHIi)−1HHIiy. Substituting ˜sIiinto d(Ii, ・), we obtain the concentrated distance metric ˜ d which
only explicitly depends on the TAC
wherePHIi= HIi(HHIiHIi)-1HHIi
From the above equation it is clear that ˜ d(Ii) measures the euclidean distance square between y and the subspace spanned by the columns of HIi, and hence can be used to measure how likely y is generated from
the TAC set Ii. From the Pythagoras’ theorem ||y2|| =||PHIjy2|| + ||(INr− PHIj)y||2, it is clear that minimizing above
equation is equivalent to maximizing ||PHIiy||2= yHPHIiy. We therefore propose a new ordering criterion which
sorts the TACs in descending order according to the associated new weighting factor uj, defined as uj= yHPHIjy,
for all j = 1, . . . ,N.
Fig. 1 shows the empirical CDF of the generated rank of the true TAC after being sorted using the original and the proposed ordering criteria, simulated over 10, 000 independent realizations. It is easily observed that the proposed ordering criterion has higher probability of generating smaller rank for the true TAC, which is advantageous for OB-MMSE detection. Despite of the superior ordering performance of the proposed ordering criterion, the main drawback lies in the increased complexity. Straightforward computation of ujrequires a
complex QR factorization, a matrix-vector multiplication, and an inner product. It can be shown that an overall complexity of O(8NNrN2p − 8 /3N N3p + 8NNrNp) real flops is required to compute all the weighting factors.
This high complexity motivates us to develop a computationally more efficient algorithm to compute these metrics as shown in the next subsection.
We first denote the channel matrix associated to the TAC I as HI = [B, c], where B and c are formed by the first Np− 1 column vectors and the last column vector of HI, respectively. By using the well known property
of projection matrices [9], we have PHI = P[B,c] = PB+P(I−PB)c, where PB and P(I−PB)c denote projection
matrices associated to the subspaces spanned by the columns of B and (I − PB)c, respectively. It follows that for
any two arbitrary vectors f ∈CNr×1 and g ∈CNr×1, one can compute from remaining equations
A summary of the proposed CECML The algorithm first initializes the elements in דhh, φh,h, and hy to
be all zeros. The (ℓ,m)th element of דhh will used to store the value of hHℓ hmafter it has been computed. The
(ℓ,m)th element of φh,h is set to 1 if [דhh]ℓ,m has been computed and stored,while the ℓth element of hy is used to
store the value of hHℓ y after it has been computed.
IV.
SIMULATION RESULTS
In this section simulation is done by MATLAB R2010a. MATLAB is a widely used programming language for algorithm development, data analysis, visualization and numerical computation. MATLAB has a long history in communication system design and used by both academic and practitioners. Many of its features and capabilities are perfect for modelling wireless systems (D. Tse and P. Viswanath, 2005): (1) it has interactive program and environment that matches the exploratory nature of science; (2) it provides seamless access to data and algorithms; and (3) it has tools for visualization, algorithm development, and data analysis.
Figure 8.BER of various GSM Detectors
Figure 9.Complexity of various GSM Detectors
V.
CONCLUSIONS
This paper studied optimalAn improved ordered block MMSE detector for generalised spatial modulation.
CECML algorithm which enables efficient computation of a newly proposed ordering metric for the OB-MMSE detector in a GSM system. The proposed algorithm avoids redundant computations and enables early termination without noticeable performance degradation. Simulation results show that the proposed algorithm provides substantial complexity reduction at moderate to high SNR region and hence exhibits a better performance-complexity tradeoff compared to the existing OB-MMSE detector.
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