Prof. Y.P.Singh, Eisha Akanksha, Shilpa N ijesird , Vol. II Issue IX March 2016/604
SPATIAL MULTIPLEXING IN MODERN MIMO SYSTEMS
1Prof. (Dr.)Y.P.Singh, 2Eisha Akanksha, 3SHILPA N
1Director, Somany (P.G.) Institute of Technology & Management ,Rewari, Haryana Affiliated to M. D. University, Rohtak, 2 a research scholar of Electronics & communication Engineering , Sunrise University, Alwar, Rajasthan
and Assistant Professor MVJCE Bangalore, 3 Assistant Professor , Department of Medical Electronics, MVJ College of Engineering, Bangalore
Abstract— Digital communication using multiple-input-multiple- output (MIMO) has been regarded as one of the most significant technical breakthrough modern communications. Beside, several different open loop MIMO systems include, Spatial Multiplexing (SM) to provide diversity gain and increase the reliability of wireless links. Under suitable channel fading conditions, having both multiple transmit and multiple receive antennas (i.e., a MIMO channel) provides an additional spatial dimension for communication and yields a degree-of- freedom gain. These additional degrees of freedom can be exploited by spatially multiplexing several data streams onto the MIMO channel, and lead to an increase in the capacity: the capacity of such a MIMO channel with n transmit and receive antennas is proportional to n.
Index Terms—Diversity, spatial multiplexing(SM or SMX) , , MIMO, SIC, ML, ZF..
I. INTRODUCTION
Historically, it has been known for a while that a multiple Input / multiple output systems consist of several transmission antennas and receiver antennas, the combination of which exploits the spatial and time dimensions of the channel and hence the multiple antennas allow spatial separation of the signals from the different users. It was observed in the mid 1990s. multiple access system with multiple antennas at the base-station allows several users to simultaneously communicate with the base-station. This holds provided that the scattering environment is rich enough to allow the receive antennas to separate out the signals from the different transmit antennas. But in wireless communication the propagation channel is characterized by multipath propagation due to scattering on different obstacle. The multipath problem is a typical issue in communication system
with time variations and time spread. For time variations the channel is fading and caused SNR variations. For time spread, it becomes important for suitable frequency selectivity. In an urban environment, these signals will bounce off trees, buildings, etc. and continue on their way to their destination (the receiver) but in different directions.
With MIMO, the receiving end uses an algorithm or special signal processing to sort out the multiple signals to produce one signal that has the originally transmitted data.
II. SPATIAL MULTIPLEXING Spatial multiplexing (seen abbreviated SM or SMX) is a transmission technique in MIMO wireless communication to transmit independent and separately encoded data signals, so-called streams, from each of the multiple transmit antennas. Therefore, the space dimension is reused, or multiplexed, more than one time. Spatial multiplexing requires MIMO antenna configuration where a high rate signal is split into multiple lower rate streams and each stream is transmitted from a different transmit antenna in the same frequency channel. If these signals arrive at the receiver antenna array with sufficiently different spatial signatures, the receiver can separate these streams into (almost) parallel channels. Spatial multiplexing can be used with or without transmit channel knowledge. Spatial multiplexing can also be used for simultaneous transmission to multiple receivers, known as space-division multiple accessing. The
Prof. Y.P.Singh, Eisha Akanksha, Shilpa N ijesird , Vol. II Issue IX March 2016/605 scheduling of receivers with different spatial
signatures allows good separability.
III. HOW DOES SPATIAL MULTIPLEXING WORK?
If the transmitter is equipped with nt
antennas and the receiver has nr antennas, the maximum spatial multiplexing order (the number of streams) is,
ns = min (nt, nr)
if a linear receiver is used. This means that ns
streams can be transmitted in parallel, ideally leading to an ns increase of the spectral efficiency (the number of bits per second and per Hz that can be transmitted over the wireless channel).
Say. You have M= 3 number of antennas in the transmitting side and have K (a1, a2, a3, a4, a5, a6) = 6 bits for sending. At first divide the bits into M=3 sub streams of data {(a1, a3), (a2, a4), (a3, a6)} and then multiply each sub stream of data with three carrier frequency in order to transmit them via three separate antennas. If all the sub-streams had to be transmitted by one carrier then the bandwidth consumptions would be three time greater-this is one of the great advantage of spatial multiplexing.
Now at the receiving end each sub-stream will have three spatial signatures-that means total 9 spatial signature will be at the receiving antenna- due to the multipath environment each sub stream will have its own spatial signature. Based on this spatial signature sub-streams of data will be demultiplexed and decoded in order to get back the original data stream-this is how spatial multiplexing works.
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Spatial Multiplexing Techniques
Goal is to Increased data rates compared to single-antenna system. At the transmitter, the data sequence is split into nt sub-sequences that are transmitted simultaneously using the same frequency band. Hence data rate increased by factor nt (multiplexing gain). At the receiver, the sub-sequences are separated by means of interference-cancellation algorithm, e.g., linear zero-forcing (ZF)/ minimum-mean-squared-error (MMSE) detector, maximum-likelihood (ML) detector, successive interference cancellation (SIC) detector. Typically, channel knowledge required solely at the receiver and for a good error performance, typically nt ≥ nr required
IV. MATHEMATICAL DETAILS A. SYSTEM MODEL
∙ Consider a MIMO system with nt transmit and nr
receive antennas
Prof. Y.P.Singh, Eisha Akanksha, Shilpa N ijesird , Vol. II Issue IX March 2016/606
y[k] = H x[k] + n[k]
Where
Transmitted vector: x[k] := [ x1[k], ..., xM [k]]T
Noise vector: n[k] := [ n1[k], ..., nN [k]
]T
Received vector: y[k] := [ y1[k], .. yN
[k]]T
Channel matrix:
h1,1
· · · h1, nt
. .
H :=
. . . . . ..
h nr,1
nr, nt
· ·
·
∙ B. ASSUMPTIONS:
– Frequency non-selective fading & square- root Nyquist filters at transmitter and receiver (pulse energy Eg := 1),
⇒ No intersymbol interference (ISI) – Rayleigh fading (no LoS component),
i.e., channel gains are zero-mean complex Gaussian random variables
– Block fading , i.e., channel gains are invariant over complete data block and change randomly from one block to the next
C. SPATIAL MULTIPLEXING
• Consider a MIMO system with nr
≥ nt > 1 antennas (For nr < nt , the system is inherently rank- deficient)
• Assume that the instantaneous realization of the channel matrix is known solely at the receiver
D. LINEAR ZF DETECTION:
Spatial interference completely removed;
however, variance of the resulting noise samples may be significantly enhanced
E. LINEAR MMSE
DETECTION:
– Usually better performance than ZF detection, since better trade-off between spatial interference mitigation & noise enhancement – For high SNR values (σn2
→0), both detectors become equivalent Performance of ZF/ MMSE detection often quite poor, unless nt ≫ nr
F. ML DETECTION
Brute-force search over all possible hypotheses x˜[k] for the transmitted vector x[k]
⇒ For Q-ary modulation scheme, there are QM possibilities
⇒ Optimal detection strategy (w.r.t. ML criterion), but very complex
G. SIC DETECTION:
– Good trade-off between complexity and performance
– Originally proposed in [Foschini'96] for the well-known BLAST scheme (`Bell-Labs Layered Space-Time Architecture')
– Assuming that the detection of xN [k] was correct, the influence of xˆN [k] can be subtracted from the (N −1)th row of (8); then symbol xN −1[k] can directly be detected, and so on ...
H. SHANNON'S LAW AND MIMO SPATIAL MULTIPLEXING
One of the key advantages of MIMO spatial multiplexing is the fact that it is a very powerful technique for increasing channel capacity at higher signal-to-noise ratios (SNR). MIMO Wikipedia:
MIMO, Precoding, Spatial multiplexing, Diversity Coding, WiMAX MIMO, information theory, channel capacity.
ROHDE&SCHWARZ, “Introduction to MIMO:
Prof. Y.P.Singh, Eisha Akanksha, Shilpa N ijesird , Vol. II Issue IX March 2016/607 Application Note” . spatial multiplexing achieves
this by utilising the multiple paths and effectively using them as additional "channels" to carry data.
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:
C = W log2(1 + S/N )
Where C is the channel capacity in bits per second, W is the bandwidth in Hertz, and S/N is the SNR (Signal to Noise Ratio).
But as a balance is required for given bandwidth, higher order modulation schemes, data rate and the allowable error rate. It is therefore necessary to look at other ways of improving the data throughput for individual channels. MIMO is one way in which wireless communications can be improved and as a result it is receiving a considerable degree of interest.
I. SPATIAL DIVERSITY
It is a technique to fight against signal fading. If a radio signal is received through one channel in a deep fading environment, then there is a possibility of losing that signal. Once a signal is lost then there is nothing that can be done to recover it. That is why diversity technique is used to improve system performance in the presence of fading channels. In this technique, signals are transmitted and received through a number of channels instead of only one channel. The main idea behind diversity is that when several copies of the same signals are passed through different channels then they experience independent fading-- since each signal pass through different physical channels or paths. There is high probability that some signals will undergo deep fading while the others may not. When these signals reach the receiver, there will be significant amount of energy to make a decision that what was actually sent. The major types of diversities are frequency diversity, temporal diversity, multipath diversity, spatial diversity.
J. DIVERSITY SIGNALS COMBINING METHODS
The main idea of diversity technique is to compare and combine different copies of the received signals-coming from independent fading channels- to increase the received power at the receiver so as signals can be interpreted correctly at the receiving ends. The most popular diversity combining methods are:
Maximum ratio combining- selects the best signal to noise ratio.
Pure selection diversity-select the best signal to noise ratio in the antenna branch.
Equal gain combining- almost the same as maximum ratio combining in performance.
REFERENCES
[1] Transmit Diversity vs. Spatial Multiplexing in Modern MIMO Systems , Angel Lozano, Senior Member, IEEE, and Nihar Jindal, Member, IEEE with and without frequency diversity,”
Bell Syst. Tech. J., vol. 49, no. 8, pp. 1827–1871, Oct. 1970.
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[7] D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2005.
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[9] C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error correcting coding and decoding: Turbo-codes,” Proc.
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[10] M. A. J. Goldsmith, “The capacity of downlink fading channels with variable rate and power,” vol. 46, pp. 569–
580, Aug. 1997.
Prof. Y.P.Singh, Eisha Akanksha, Shilpa N ijesird , Vol. II Issue IX March 2016/608
Mrs. Eisha Akanksha a research scholar is post graduate in Electronics & Communication engineering , working as Astt.
Professor In MVJCE Bangalore, She is pursuing her research in the innovative and front line reserach in Energy efficient communication system (a future wireless internet communication), from the Faculty of Computer Engineering Electronics and communication Engineering, Sunrise University, Alwar, Rajasthan
1.Prof. Y.P. Singh, currently working as Director, Somany (P.G.) Institute of Technology and Management , Rewari, Haryana . He has also worked about 27 years as Lecturer, Dean of academics &
Principal in many Engineering institutions and organization. He has also served with Training and Technical Deptt. Govt.Of Delhi, almost for 17 years. He has about 43 research paper published in National and 48 papers published in international journals in his credit. He has been selected and awarded by Govt. of Delhi as “Best Technical Teacher-2004”. He has been conferred Out standing Teacher Award 2012 and 2013 respectively. He is also an expert and Master Trainer for the Teachers, empanelled by SCERT/NCERT. He is also the guide of research scholar for almost dozen of Universities.
Mrs.Shilpa N is a Post Graduate in Bioinformatics from Oxford Group of Institutions, Bangalore and Presently working as a Assistant Professor , MVJ college of Engineering, Bangalore.
