How Adaptive Algorithm Work With Mimo/Ofdm: Research Paper Examples
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Multiple-input multiple-output (MIMO) systems technology has been used in combination with OFDM (orthogonal frequency division multiplexing) to come up powerful air-interface solutions for use in next-generation digital wireless communication. MIMO/OFDM technology has been adopted by different wireless standards such as Wi-Max, W-LAN (wireless local area networks), and 3G-LTE and 4G-LTE (third and fourth generation Long-Term Evolution) mobile technology. This is because MIMO/OFDM-based channels offer high data transmission rates over multiple paths and frequency selective fading channels (Kakadiya, Solanki, Hadia & Rathod, 2013).
How Adaptive algorithms work with MIMO/OFDM:
Mechanism of work:
Channel estimation in MIMO-OFDM systems is a difficult task due to frequent changes in channel parameters. It is, therefore, important that an appropriate channel estimation technique be found to track the changes in parameters using a variety of adaptive algorithms.
The adaptive filter in an algorithm can be based on recursive least square (RLS), least mean square (LMS), or linear-quadratic estimation (LQE) also known as KALMAN that does not need prior knowledge of noise and channel SOS (second order statistics). RLS adaptive estimation with adaptive forgetting factor is very effective in frequency selective fading. LMS adaptive estimation is mostly used to bolster channel performance but due to its serious estimation errors and low convergence speed, it is used together with KALMAN to provide more accurate estimates. However, transferring LMS results to the KALMAN filter increases the computational complexity. To reduce computational complexity, adaptive filter based algorithms use MMSE (minimum mean square error) or LS (least square) methods (Kakadiya et al., 2013).
Multiple input and multiple output (MIMO):
MIMO technology uses multiple transmission antennas and multiple receiver antennas to improve spatial diversity and consequently improve carrying capacity.
Functions of MIMO:
MIMO has three main functions i.e. precoding, spatial multiplexing and diversity coding.
Precoding: refers to an abstraction of beam forming that supports multi-layer transmissions in wireless communications using multiple antennas. In traditional single layer beam forming, each of the transmitting antennas transmits the same signal but using appropriate weighting that maximizes signal power at the receiver output. When there are multiple receiving antennas, single layer beamforming cannot maximize signal levels at all receiving antennas simultaneously. Multi-layer beam forming is thus required for throughput maximization in multiple receive antenna systems. Beamforming has various benefits such as increasing receiving signal gain by adding up signals from different antennas constructively, and reduce multipath frequency fading. Precoding can be classified as single or multiple user MIMO (Gesbert, Kountouris, Heath Jr., Chae & Salzer, 2007).
Spatial Multiplexing: Involves splitting a high rate signal into multiple lower rate streams and each of the lower rate streams is transmitted from a different antenna but of the same frequency channel. If the sent signals arrive at the multiple receiving antennas with sufficiently varied spatial signatures, the streams are separated into parallel streams by the receiver. Spatial multiplexing is a powerful method for increasing channel capacity at higher SNRs (signal to noise ratios) (Gesbert et al., 2007).
Diversity Coding: Diversity coding techniques apply best when the transmitter does not have prior knowledge of the channel. In this MIMO function, a single stream is transmitted but the signal is coded using space-time coding techniques. Each transmitting antenna emits signals with near orthogonal or full coding. Diversity coding also exploits independent fading observed in multiple antenna links thus diversifying the signal more. Since channel knowledge does not exist, there is no array gain or beam forming observed from diversity coding (Gesbert et al., 2007).
Forms of MIMO:
Multi-antenna types: This form of MIMO is essentially classified according to the number of antennas. Conventional multi-antenna MIMO (single user MIMO) has multiple transmitting antennas and multiple receivers. The technology is mostly used in 802.11n wireless LAN standards. Other special forms of MIMO classified based on antenna number include multiple-input-single output (MISO) where there is a single receiving antenna. Single input and multiple-output is one where the transmitting antenna is only one but with multiple receiving antenna (Gesbert et al., 2007).
Multi-user types: In multi-user MIMO, also known as MU-MIMO, one base station acts as a communication link between multiple users. On the MIMO broadcast channel (downlink) the station sends out multiple data streams to users, and on the uplink base station, different data streams are received from users. MU-MIMO has variations that involve partial or full data multi-casting, and while it is usually applied to cellular communications, MU-MIMO has a potential application in WLAN and wireless ad hoc networks (Heath Jr, 2014).
Applications of MIMO:
MIMO is currently applied to all wireless products supporting the 802.11n WLAN standard (Intel, 2015). It has also been integrated into other standards such as WMAN (Wi-MAX 802.16e), Radio Frequency Identification (RFID), 4G cellular networks, and mesh networks such as Muni-Wireless (Kakadiya et al, 2013).
Orthogonal frequency-division multiplexing (OFDM):
Orthogonal Frequency Division Multiplexing is a signal modulation technique that splits high data rate modulating streams and places them into several narrowband close-spaced subcarrier channels that are slowly modulated thus ensuring less sensitivity to frequency selective fading (Radio-electronics.com, 2015).
OFDM is currently being used in digital wireless and telecommunications standards. It has especially been adopted in wireless standards such as 802.11a, 802.11ac, and 802.11n. In cellular networks, OFDM has been used in high-speed telecommunications standards such as LTE and LTE-A. Recently Wi-MAX technology has also adopted OFDM.
Orthogonal frequency division multiplexing is now being used in digital broadcasting standards such as DVB (Digital Video Broadcast) for television and DAB (Digital Audio Broadcast) in radio. It is also used in other broadcasting systems such as Digital Radio Mondale that is used for short, medium and long wave frequency bands.
Source information in OFDM is split into multiple carrier frequencies known as sub-channels during signal transmission. The key features offered by OFDM are also described as its advantages.
Advantages of OFDM:
OFDM uses multiple sub channels and is thus insusceptible to frequency selective fading that is caused by wave delay in some sub-channels.
ODFM can exploit both frequencies interleave and time interleave to improve forward error correction (FEC) effectiveness.
ODFM has long guard and symbol intervals that reduce the issues arising from delayed wave interference. Guard and symbol intervals refer to the time interval within which retransmission of part of the signal sequence occurs.
ODFM has a dense spectrum that enables it to have a higher spectrum efficiency that is better than single carrier systems. The more the number of sub-channels in ODFM, the better the system meets the minimum Nyquist bandwidth requirements.
It is possible to assign information arbitrarily to specific subchannels in OFDM. This is quite helpful especially when trying to avoid subchannels where signal interference is expected (carrier holes) thus adding system flexibility.
Hierarchical layering of information is made easier by the modulation of each sub-channel and power modification capabilities.
Of the advantageous features of OFDM discussed above, OFDM robustness to delayed wave interference is the most critical especially since ODFM is used in DAM transmissions (Shiomi & Hatori, 2000).
Disadvantages of OFDM:
Despite its numerous beneficial features, OFDM has the following limitations:
OFDM is a multicarrier system, and thus any nonlinearities in the transmission channels lead to cross modulation that reduces overall system performance.
In order to preserve the orthogonal orientation of carrier signals, a complex transmitter/receiver design is required. In fact, the receiver requires special design measures to ensure accurate signal regeneration when channel conditions fluctuate (Shiomi & Hatori, 2000).
OFDM Characteristics and Principles of Operation:
Sub-Channel Carrier Orthogonality:
OFDM communications utilize the frequency spectrum effectively by using sub-carriers that partially overlap without interposing on adjacent sub-carriers since each sub-carriers maximum power directly corresponds to each adjacent channel’s minimum power. The Figure 1 below shows a graphical representation of the ODFM system frequency domain. A different peak represents each sub-carrier, and additionally, each sub-carrier peak directly corresponds to the zero crossing of all other channels.
It should be noted that OFDM channels vary from band-limited FDM only channels in the application of pulse-shaping filters. Conventional FDM systems apply a sinc-shaped pulse to the time domain that shapes each symbol and prevents intersymbol interference (ISI).
In OFDM systems, the sinc-shaped pulse is instead applied to each channel’s frequency domain thus making each sub-carrier orthogonal to the other.
Figure 1: ODFM system frequency domain showing Orthogonality (Source: National Instruments, 2014)
Serial to Parallel Conversion:
In OFDM, each channel can be split into several sub-carriers that optimizes the frequency spectrum usage but also necessitates additional processing by the transmitter and receiver. The extra processing is needed to convert a single serial bit-stream into a series of parallel bit streams that are then split among the individual sub-carriers. After the bit-stream has been split among sub-carriers, each sub-carrier then undergoes modulation as an individual channel before all the channels are recombined and transmitted as one. Upon receiving the channel transmission, the receiver reverses the process by splitting the incoming signal into its constituent sub-carriers then demodulating them in order to reconstruct the original bit-stream transmitted.
Inverse FFT Modulation:
The inverse Fast Fourier Transform (IFFT) transmission stage is where data modulation into a complex waveform occurs. It is possible to choose a modulation scheme that is independent of the specific channel in use and can also be chosen to meet channel requirements. Each sub-carrier can have its modulation scheme. IFFT thus plays a role in modulating each sub-channel then mapping it onto the appropriate sub-carrier.
Insertion of Cyclic Prefix:
Wireless communications are predisposed to multi-path channel reflections, and thus a cyclic prefix is added for ISI reduction. The cyclic prefix is essentially a repetition of a symbol’s first section and is appended to the same symbol’s end. The cyclic prefix is used to enable fading of the original signal’s multipath representation to eliminate interference with the next symbol. The Figure 2 Below demonstrates how this is done.
Figure 2 Cyclic Prefix Insertion (Source: National Instruments, 2014)
Parallel to Serial Conversion:
After adding a cyclic prefix to the sub-carrier channels, they must be transmitted as a single signal. Parallel to serial conversion involves summing up all sub-carriers and combining them into one signal. This results in the almost perfect and simultaneous generation of sub-carriers at the receiver.
OFDM Idealized system model:
In order for an OFDM communications systems to utilize multiple sub-carriers in the transmission of a single channel, the system must perform some steps at both the transmitter and receiver end. These steps essentially correspond to the characteristics and operation principles of OFDM earlier discussed in this paper. For this reason, this section will only depict a graphical representation of how OFDM works in the Figure 3 shown below.
Figure 3 Idealized OFDM system showing Transmitter/Receiver Implementation
Bhoyar, D., & Niranjane, V. (2012). Channel Estimation for MIMO-OFDM Systems. International Journal of Engineering Research and Applications (IJERA), 2(1), 44-50.
Gesbert, D., Kountouris, M., Heath Jr., R., Chae, C., & Salzer, T. (2007). Shifting the MIMO Paradigm. IEEE Signal Process. Mag., 24(5), 36-46. doi:10.1109/msp.2007.904815
Heath Jr, R. (2014). Multiuser MIMO. Profheath.org. Retrieved 10 March 2015, from http://www.profheath.org/mimo-communication/multiple-user-mimo/
Intel,. (2015). Wireless Networking - What Is Multiple-Input Multiple-Output (MIMO)?. Intel.com. Retrieved 11 March 2015, from http://www.intel.com/support/wireless/sb/cs-025345.htm
Kakadiya, A., Solanki, M., Hadia, S., & Rathod, J. (2013). Analysis of adaptive channel estimation techniques in MIMO-OFDM system. InternationalÂ JournalÂ OfÂ AdvancementsÂ InÂ ResearchÂ &Â Technology, 2(4), 352-353.
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Shiomi, T., & Hatori, M. (2000). Digital broadcasting (pp. 113-114). Chiyoda-ku, Tokyo: Ohmsha Ltd.
Uthansakul, P., & Bialkowski, M. (2006). An Adaptive Power and Bit Allocation Algorithm for MIMO OFDM/SDMA System Employing Zero-Forcing Multi-user Detection. African Journal Of Information And Communication Technology, 2(2), 63-70.
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