[Article] Implementation of a MIMO OFDM-Based Wireless LAN System

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It is also observed that the modulator incurred a higher processing time T Mod than the demodulator T Dmod. This is because more mathematical operations are involved in modulating the signal than demodulating. The spectral efficiency can be calculated using Equation 17 for various E b N o values. It can be observed from Equation 17 that by increasing E b N o , the spectral efficiency will increase.

The energy efficiency EE versus spectral efficiency SE graph for different modulation techniques is shown in Figure 9. Energy efficiency vs. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online May Author information Article notes Copyright and License information Disclaimer. This article has been cited by other articles in PMC. Introduction Due to advancement in Micro-Electro-Mechanical Systems MEMS technology, low power and low cost WSNs can be deployed in many real life applications, including environmental monitoring, home automation, traffic control, precision agriculture and health care [ 1 — 3 ].

Massive Mimo Channel Estimation Matlab Code

Background 3. Multi-Carrier Modulation FOFDM is a multi-carrier modulation technique in which a high data rate substream is demultiplexed into lower data rate substreams to increase the duration of each substream so that inter-symbol interference ISI can be reduced. Open in a separate window. Figure 1. Figure 2. Figure 3.

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Figure 4. Parametric Modeling of System Characteristics 5. Energy Consumption In [ 8 , 14 ], the energy consumed in baseband signal processing blocks were neglected to keep the energy consumption model simple. Figure 5. Figure 6. Figure 7. Figure 8. Table 1. Base Band Digital Energy Consumption. Table 2. Table 3. Energy Consumption in Transmit and Receive Modes. Table 4. Time Delay. Figure 9. Notes Conflict of Interest: The authors declare no conflict of interest.

References 1. Akyildiz I. Wireless sensor networks: A survey. Yick J. Wireless sensor network survey. Guo H. Optimizing the localization of a wireless sensor network in real time based on a low-cost microcontroller. IEEE Trans.

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Gezer C. An IEEE Misra S. A survey of multimedia streaming in wireless sensor networks. IEEE Commun. Wireless multimedia sensor networks: Applications and testbeds. Multiple branch successive interference cancellation for MIMO spatial multiplexing system: Design, analysis and adaptive implementations. IET Commun. Cui S.

IEEE J. Areas Commun. Wang H. Asynchronous cooperative communication systems: A survey on signal design. China Inf. Alamouti S. A simple transmit diversity technique for wireless communications. Jiang J. A singular-value-based adaptive modulation and cooperation scheme for virtual-MIMO systems.

Performance assessment of virtual multiple-input multiple-output systems with compress-and-forward cooperation. Apply beamforming to improve the link-level performance. Apply transmit beamforming to focus energy towards a receiver. Use receive beamforming to improve the SNR by pointing receiver's main beam towards transmitter. Perform transmitter modulation accuracy and spectrum emission mask and flatness measurements. Perform frame synchronization, frequency offset correction, channel estimation and equalization, and common error phase tracking.

Demodulate and decode signaling and data fields. Perform waveform generation and end-to-end link level simulation for the IEEE Specify resource unit RU allocation. Use WLAN customizable and editable algorithms as golden references for design verification. Use the comprehensive set of transmitter, channel model, and receiver operations that are expressed as open and customizable MATLAB code.


Generate C code to accelerate simulation, obtain C source code for implementation, or use as a standalone executable. Connect your transmitter and receiver models to radio devices, and verify your designs via over-the-air transmission and reception. Simulate multilink Generate HE-format packets with packet extension based on Draft 3. Choose a web site to get translated content where available and see local events and offers.

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Other MathWorks country sites are not optimized for visits from your location. Wireless LANs are more appropriate option for distributed nature of communication in tactical environment. However, they are limited by range of communication. OFDM as a physical layer technology is used in These standards are primarily targeted for indoor based low range wireless communication systems. To some extent, transmitters by increasing its transmission power can achieve longer range at the cost increased RF radiation in WLAN. Increasing the Tx-power also increases the amount of distortions Ex: Error Vector Magnitude originating at the Tx-side.

Besides there is regulatory constraints that enforce a maximum equivalent isotropic radiated power EIRP [2]. Beyond that bit errors will increase at receiver. One general way to enhance the range in distributed communication systems is multi-hop communication at the cost of reduced data rate because of routing information overhead between nodes. This inturn effects the communication range of the system. Hence, it is very important to correct transmit impairments to maximum extent for achieving longer range.

A Wireless transmitter performance is dependent on several parameters such as precision of IQ samples in digital according to bit width of DAC, design choices in RF section and impairments due to Noise figure of RF chain, and board Layout. Reducing the EVM without reducing the transmit power is a challenge. EVM parameter plays a vital role in capturing the transmitter losses. The causes of EVM degradations can be categorized into two broad levels.

One is at design level or hardware level and second one is at algorithm level.

At design level, EVM can be reduced by proper hardware design choices such as impedance matching circuitry between the RF chain and antenna. Assuming that hardware losses are minimal, the EVM can be reduced at algorithm level.

Performance Analysis of Cooperative Virtual MIMO Systems for Wireless Sensor Networks

The numerical representation of the outputs and inputs of this block affects the transmission quality. The fixed point representation in IFFT leads to efficient usage of hardware resources but precision is compromised.

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On the other hand, Floating point representation improves the precision at the cost of more resource utilization on FPGA. Block floating point scaling BFPS is tradeoff between these two methods. Increasing the transmission quality proportionally increases the power output we get from power amplifier output. Therefore, it helps in increasing the range of communication. In order to understand and optimize these metrics for achieving better range or throughput, prototyping is very much essential step.

In addition, prototyping helps in understanding the implementation trade-offs and the system behavior [3]. Prototype development involves the usage of third party IP cores apart from our own logic to expedite the implementation. Altera provides user friendly IP cores, which can be used along with our custom functionality blocks. In all the architectures, Cooly-tuckey radix-r algorithm of decomposition is used for computing the IFFT in number of phases.

Increasing the radix of the decomposition leads to a reduction in the number of passes required through the FFT processor at the expense of device resources. Only difference between the architectures is how to feed input and how to take back outputs. The input and output samples can be represented in fixed point, floating point, or block floating point representations for different architectures.

In a fixed-point representation, the data precision needs to be large enough to adequately represent all intermediate values throughout the transform computation. For large FFT transform sizes, an FFT fixed-point implementation that allows for word growth can make either the data width excessive or can lead to a loss of precision. In a floating point representation, each number is represented as a mantissa with an individual exponent while this leads to greatly improved precision, floating-point operations tend to demand increased device resources.

BFP architecture is a trade-off between fixed-point and full floating-point architecture. Unlike an FFT block that uses floating point arithmetic, a block-floating-point FFT block does not provide an input for exponents. Internally, a complex value integer pair is represented with a single scale factor that is typically shared among other complex value. After each stage of the FFT, the largest output value is detected and the intermediate result is scaled to improve the precision.

The exponent records the number of left or right shifts used to perform the scaling. They are radix-4 quad engine and radix-4 single engine. To minimize transform time, altera recommends to use a quad-output engine [4]. Quad-output refers to the throughput of the internal FFT butterfly processor. The engine implementation computes all four radix-4 butterfly complex outputs in a single clock cycle. In this paper, a prototype of customized We also propose a prototype baseband architecture, which is appropriate for the design of WLAN This reduction in EVM is achieved through following methods.

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