Rf and if digitization in Radio Receivers: Theory, Concepts, and Examples
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Chương-3, tham-số-hiệu-năng, OFDM vs OFDMA
| 3.1 Processors
Many different processors are available to provide digital signal processing. These processors vary substantially in speed of operation, physical size, and cost. Speed is usually a critical requirement in selecting a processor. Other factors including dynamic range, arithmetic precision, cost, and size are also important considerations when choosing a processor. A common method used to increase total processing speed beyond that of a single processor is to employ multiple processors operating in parallel. Assuming a given processor, by putting more and more of these processors together and operating them in parallel, higher and higher processing speeds can be achieved. This, of course, also increases power consumption, size, and cost.
Many radio receiver applications require processors with small physical size and relatively low cost. For these cases, single chip processors are the preferred choice. Single chip processors can be general purpose microprocessors (such as the Intel 80486), digital signal processors (such as the Texas Instruments TMS320C40), or specialized integrated circuits for dedicated processing tasks (such as the Harris HSP50016 Digital Downconverter). Some specialized radio receiver applications may not be bound by stringent physical size and cost limitations. Therefore, processors of all types are considered in this report, ranging from single chip general purpose microprocessors to supercomputers. Computations in digital signal processing can be performed using fixed-point arithmetic or floating-point arithmetic, although many of the computations require floating-point arithmetic. The advantage of floating-point arithmetic over fixed-point arithmetic is that it permits the use of numbers with a much greater dynamic range. This is important in many digital signal-processing operations.
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In fixed-point arithmetic, the position of the decimal point in the register where each operand is stored always is assumed to be the same. In floating-point arithmetic, each operand is represented by a number stored in a register representing a fraction or integer. A number stored in a second register specifies the position of the decimal point of the number stored in the first register. Some processors do not have floating-point hardware and require floating-point operation to be implemented in software. Software implementation of floating-point arithmetic is typically much slower than hardware implementation. Because floating-point operations are so important in digital signal processing, the speed of processors is often specified in terms of millions of floating-point operations per second (MFLOPS). This parameter allows comparison of the processing speed of different processors and also allows determination of the time required to execute certain algorithms. Many different benchmarks (such as the SPEC benchmarks, Whetstone, Dhrystone, and Linpack) are used to compare speeds between processors. Each benchmark provides a number indicating the relative speed of processing based on testing varying tasks. Results from the application of a benchmark to different processors can be compared. However, results between different benchmarks, in general, should not be compared. While these benchmarks are useful for comparing processor performance, the parameter chosen to compare processing speeds between processors in this report is the theoretical peak MFLOPS. This parameter was chosen due to its ease of availability for virtually all floating-point processors, its lack of dependence on specific benchmarking algorithms, and its relevancy to dedicated applications used in implementation of a radio receiver. The theoretical peak MFLOPS parameter gives the maximum possible speed of performance for the processor. It is found by computing the number of floating-point additions and multiplications (using the processor’s full precision) that can be performed during a given time interval [25]. Some examples of the processing speed of various types of processors ranging from single chip processors to supercomputers are presented in Table 3. This table only gives a sampling of the range of capabilities that exist in digital signal processing. Many other processors, with varying capabilities, either exist or have been proposed. In addition, new developments with increasing capabilities are announced all the time. An extensive listing of processing speeds for many different computers is found in [25]. In certain situations, especially in the high-throughput case, overall processing performance is not limited by the processor speed but by the maximum data transfer rates of the peripheral components such as memory or I/O (input/output) ports. The inclusion of these factors in platform evaluation should not be ignored when choosing a processor. Table 3. Examples of Processing Technology Processing Speed* Number of Processors Platform Manufacturer and Model 50 MFLOPS 1 DSP Chip Texas Instruments TMS320C40 28
Processing Speed*
Number of Processors Platform Manufacturer and Model 120 MFLOPS 1 DSP Chip Analog Devices ADSP-21060/62 400 MFLOPS 8 VME Board Pentek 4285 800 MFLOPS 16 Computer Workstation SUN Sparc 2000 6.48 GFLOPS 4 Supercomputer Convex C4/XA-4 32 GFLOPS 4 Supercomputer Hitachi S-3800/480 184 GFLOPS 3680 Massively Parallel Computer Intel Paragon XPS140 236 GFLOPS 140
Massively Parallel Computer National Aerospace Laboratory Numerical Wind Tunnel (Japan) * Theoretical peak processing speed.
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