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Digital Signal Processing (DSP) for wireless communications

The first-generation (1G) cellular wireless mobile system were analog and were based on frequency-division-multiple access (FDMA) technology. The second boost for the cellular industry came from the introduction of the second-generation (2G) digital technology standards, including Global System for Mobile (GSM), IS-136 (Time Division Multiple Access, TDMA), and Personal Digital Cellular (PDC). This boom in the digital technology in second generation required operation on data streams, more MAC (complex multiply accumulate), ACS (accumulate compare
select), and some other operations. Hence, gives rise to the idea of DSP in wireless and mobile.

 

Digital Signal Processors (DSP) take real-world signals like voice, audio, video, temperature, pressure, or position that have been digitized and then mathematically manipulate them. Signals need to be processed so that the information that they contain can be displayed, analyzed, or converted to another type of signal that may be of use. Although real-world signals can be processed in their analog form, processing signals digitally provides the advantages of high speed and accuracy.

 

DSP technology uses specially designed programs and algorithms to manipulate analog signals and produce a signal that is higher-quality, less prone to degradation or easier to transmit. This typically requires the DSP to perform a large number of simple mathematical functions (addition, subtraction, multiplication, division, and the like) within a fixed or constrained time frame.

 

Signal processing has always played a critical role in the research and development of wireless communication systems.  In the course of the wireless revolution, as the demand for high capacity and high reliability systems increases, signal processing has an even more important role to play.

 

For example, The coder used in GSM phase-1 compressed the speech signal at 13 kb/s using the rectangular pulse excited linear predictive coding with long term prediction (RPELTP). Most general-purpose DSPs cannot support all the baseband functions, as the total required processing power for GSM itself is 53 million instructions per second (MIPS). As DSPs became more powerful, they started to take on other physical layer-1 tasks until single DSP was powerful enough to do all the DSP functions.

 

On the other hand, communicating over wireless channels presents a formidable challenge to signal processing. The wireless channels introduce time varying interference of various types: interference from multipath propagation, interference from other users and interference from other services. In order to provide reliable communication over a spectrum of limited bandwidth and under strict power constraints, sophisticated signal processing techniques are necessary to cope up with various issues ranging from efficient source, and channel coding to modulation and
receiver designs.

 

On the other hand, communication signals and systems are manmade and there are ample structures that can be exploited for high-performance algorithms. Due to advent in Digital Signal Processors technology side, low cost and low-power DSP makes implementation of highly advanced signal processing algorithms a reality.

 

DSP Algorithms for wireless communications

The digital front-end (DFE) is the most critical stage in a wireless base-station. The DFE along
with the analog to digital converter (ADC) is responsible for bridging the analog RF and IF
processing on one side and the digital baseband processing on the other side. The most important
reason for replacing analog with digital signal processing is the ability to softly reconfigure the
channels in the base station RF in real time, thus allowing for the implementation of various
signal conditioning, compensation and mitigation channel non-linear responses.

 

Blind equalization

The traditional techniques for channel estimation and equalization use training data, which not only consumes a significant portion of available bandwidth but also requires a perfect co-operation between the transmitter and receiver. In recent years, the so-called blind techniques have been explored intensively in the literature. The blind techniques do not use any training data except for certain prior information inherent in the original strings of symbols, which hence saves the bandwidth and relaxes the relationship between the transmitter and receiver. Consequently, the blind techniques have a clear potential to increase the capacity and reliability of wireless
systems. The problem of blind system/channel identification has attracted significant interest recently due to its potential applications in wireless communications. As a result, many blind channel estimation algorithms have been developed.

 

Space-time signal processing for wireless

In addition to additive noise, another problem, which has plagued communication channels, is fading. Communication in the presence of channel fading has been one of the most challenging issues in recent times. In a fading channel, severe attenuation makes it impossible for the receiver to determine the transmitted signal. One way to overcome this is to make several replicas of the signal available to the receiver with the hope that at least some of them are not severely attenuated. This technique is called diversity. It is perhaps the most important contributor to reliable wireless communication.

 

A simple space diversity scheme, which does not involve any loss of bandwidth, is to use multiple antennas at the receiver. The optimal way (in terms of Signal-to-Noise-Ratio [SNR]) to combine the outputs of different antennas is the maximal ratio combining.

 

In space-time, modems operate simultaneously on all the antennas. The key leverage that we get from this scheme is that the co-channel interference can be significantly reduced in a way, which is not possible with single antenna modems. The reason why this is possible is that the co-channel interference and the desired signal almost always arrive at the antenna array (even in complex multipath situation) with distinct and well separated spatial signatures, thus allowing the modem to exploit this difference to reduce the co-channel interference. Likewise, the space-time transmit
modems can use spatial selectivity to deliver signals to the desired mobile while minimizing the interference for other mobiles.

 

Digital front end (DFE)

An important part of digital front end (DFE) used for RF communication systems is the Digital
Up Converter (DUC). The main function of the DUC is to convert one or more channels of
data from baseband format to a pass-band signal consisting of modulated carriers belonging to a
set of one or more specified radio or intermediate (RF or IF) frequencies. This is achieved in two
steps: (i) increasing of the sampling rate by interpolation, providing spectral shaping and rejection of interpolation images by means of filtering, and (ii) shifting the signal spectrum to the desired carrier frequencies using RF mixers and Voltage Controlled Oscillators (VCOs).

 

In the recent days the wireless industry has been trying to rapidly reduce Capital Expenditure
(CAPEX) and Operating Expenditure (OPEX). It has been determined that up to 60 percent of
the overall CAPEX cost is due to the radio elements within a typical base station RF. In addition
to this, the base station RF also contains the power amplifiers which are responsible for a large
percentage of the OPEX cost that is incurred.

 

The CAPEX can be reduced through the use of low cost non-linear power amplifiers, and highly integrated digital RF transceivers. . OPEX can be further reduced through the use of
advanced, efficient algorithms, as OPEX is directly related to the power amplifier efficiency in
the base station.

 

Presently, only a small proportion of the DC power being consumed by the base station is converted into radiated energy. The transmitted signal generally determines the efficiency at which the power amplifier operates. WCDMA/LTE signals normally tend to have a high Peak-to-Average Power Ratio (PAPR) or Crest Factor. This tends to impose severe restrictions on the operations of the power amplifier. Thus to increase the power amplifier efficiency, CFR algorithms are used to decrease the PAPR of the transmitted signal before it enters the power amplifier

 

Crest factor reduction (CFR) schemes help reduce the Peak to Average Power Ratio (PAPR)of the signal entering the base-station and have been implemented widely for code division multiple access (CDMA) and Long Term Evolution (LTE) systems, this is important because if the signal with the high PAPR is allowed to pass through the power amplifier(PA) it will result in the amplifier operating in its nonlinear region creating non-linear distortions in amplitude and phase, and the only other way to avoid this is to back off the signal to the linear region of the amplifier thus reducing its efficiency.

 

In addition to the method mentioned above Digital Pre-Distortion (DPD) can be used to increase
the efficiency of the power amplifier. Over here instead of using digital signal processing to
reduce the dynamic range of the transmitted signal as is done in CFR, DPD algorithms tend to
linearize the power amplifier itself

 

Digital Pre-Distortion (DPD) is one of the most fundamental building blocks in wireless communication systems today. It is used to increase the efficiency of Power Amplifiers. By reducing the distortion created by running Power Amplifiers in their non-linear regions, Power Amplifiers can be made to be far more efficient. Wireless base stations not employing CFR or DPD algorithms typically exhibit low efficiency, and therefore high operational and capital equipment costs. A typical Class AB LDMOS Power Amplifier with WCDMA waveforms may have approximately 15-20% efficiency. With CFR and DPD turned on, this efficiency can grow to as much as 40%, resulting in tremendous savings in CapEx and OpEx for network operators.

 

Interference Cancellation

With the rapid development of new wireless communication systems, the size of cell area is getting smaller and the problem of crosstalk between cells, and between base stations and users becomes more severe. An example of interference in the LTE cellular communication system when the user equipment (UE) approaches the border of the two adjacent cells, there is a chance that there might be another user in the neighboring cell using the same frequency band for communication. This situation can cause interference to both users utilizing the same frequency band and dramatically reduce the data rate.

 

To address this problem, the 3GPP consortium decided that the users at the same cell edge but belonging to different cells, use different frequency resources. Base stations that support this feature can generate interference information for each frequency resource (RB), and exchange the information with neighbor base stations through messages. Although this problem can fix the issue in low population areas, it does not work in crowded cells since the demand for empty bands increases, and the base has to use all the available bands for communication.

 

To overcome this crosstalk, the common solution is that the receiver sends feedback to the transmitter which then responds by adjusting the transmission frequency. However, these feedback loops increase the latency and penalize real-time applications where low latency is required such as autonomy and augmented reality (AR).

 

There are several traditional digital signal processing (DSP) techniques to remove a narrow band interference from the desired wideband signal. However, they are not effective for the case of wideband interference, where the interference, can occupy the same bandwidth as the desired signal. In the OFDM/QAM modulation wireless systems, each resource element (RE) can have a discrete value from the QAM symbol space. If this is the case for both UE and the interferer, the combination of both of these two will result in a discrete sample space too.

 

Using a Maximum Likelihood Estimation (MLE) approach for the classification of this new sample space requires a lot of computation and might not be practical. Conventional DSP techniques recover distorted signals by cascade operation of hand-crafted physics models to mitigate specific impairments sequentially,

 

Cite This Article

 
International Defense Security & Technology (October 5, 2022) Digital Signal Processing (DSP) for wireless communications. Retrieved from https://idstch.com/technology/ict/digital-signal-processing-dsp-for-wireless-communications/.
"Digital Signal Processing (DSP) for wireless communications." International Defense Security & Technology - October 5, 2022, https://idstch.com/technology/ict/digital-signal-processing-dsp-for-wireless-communications/
International Defense Security & Technology September 12, 2022 Digital Signal Processing (DSP) for wireless communications., viewed October 5, 2022,<https://idstch.com/technology/ict/digital-signal-processing-dsp-for-wireless-communications/>
International Defense Security & Technology - Digital Signal Processing (DSP) for wireless communications. [Internet]. [Accessed October 5, 2022]. Available from: https://idstch.com/technology/ict/digital-signal-processing-dsp-for-wireless-communications/
"Digital Signal Processing (DSP) for wireless communications." International Defense Security & Technology - Accessed October 5, 2022. https://idstch.com/technology/ict/digital-signal-processing-dsp-for-wireless-communications/
"Digital Signal Processing (DSP) for wireless communications." International Defense Security & Technology [Online]. Available: https://idstch.com/technology/ict/digital-signal-processing-dsp-for-wireless-communications/. [Accessed: October 5, 2022]

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