XT-11 Bluetooth Earphone Magnetic Wireless Sports Headset Bass Music Earbuds Mic for Mobile Phones and More Devices
Nobuyoshi Terashima, in Intelligent Communication Systems, 2002
5.4.4 Satellite Communication System
A satellite communication system involves a transmitter and a receiver installed at each station. A server sends information to its destination via satellite and receives a response from the destination via satellite. Between a source and its destination, an in-bound channel and an out-bound channel are established to conduct communication. In the distance education, two channels are established between a main campus and a satellite campus. When there are two or more sites, the number of channels is increased, making this type of setup very expensive.
One-way transmission is widely used in distance education. In this system a transmitter is installed at the main campus, with receivers installed at satellite campuses. The lectures are broadcast from the main campus to the satellites simultaneously. This system does not allow a question-and-answer session. However, Q&A between main campus and satellites can occur when they are connected via the Internet. Thus, the Q&A is conducted over the Internet and the lectures are broadcast via satellite. Figure 5.7 shows a diagram of the satellite/Internet system.
FIGURE 5.7. Distance learning system via satellite.
Thomas L. Norman CPP, PSP, CSC, in Effective Physical Security (Fifth Edition), 2017
All satellite communications insert very high latency, typically more than 240 ms. This is useless for pan/tilt/zoom use, and the insert delay makes voice communications tricky. However, if you cannot get communications there any other way, it is a real blessing. For unmanned offshore oil platforms, when connected to an alarm system, the video can confirm that a fishing vessel has moored itself out 100 miles from shore and that a break-in on the platform is occurring. Combined with voice communications, those intruders can be ordered off the platform in virtual real time with an indication that the vessel number has been recorded. During high-security levels, the video system allows for a virtual “guard tour”of the platform, without the need to dispatch a helicopter at a cost of thousands of dollars for each sortie.
Daniel S. Soper, in Computer and Information Security Handbook, 2009
Depending on the type of satellite communications link that needs to be established, substantially different technologies, frequencies, and data encryption techniques might be required. The reasons for this lie as much in the realm of human behavior as they do in the realm of physics. It is not unreasonable to conclude that satellite encryption would be entirely unnecessary if every human being were perfectly trustworthy. Barring a desire to protect messages from the possibility of extraterrestrial interception, there would be no need to encrypt satellite communications if only those individuals entitled to send or receive a particular satellite transmission actually attempted to do so. In reality, human beings, organizations, and governments commonly possess competing or contradictory agendas, thus implying the need to protect the confidentiality, integrity, and availability of information transmitted via satellite. With human behavioral considerations in mind, the need for satellite encryption can be evaluated from a physical perspective. Because the sender of a satellite message may have little or no control over to whom the transmission is made available, protecting the message requires that its contents be encrypted. Aside from these considerations, the sensitivity of the information being transmitted must also be taken into account. Various entities possess different motivations for wanting to ensure the security of messages transmitted via satellite.
Daniel S. Soper, in Computer and Information Security Handbook (Third Edition), 2013
2 The Need for Satellite Encryption
Depending on the type of satellite communications link that needs to be established, substantially different technologies, frequencies, and data encryption techniques may be required in order to secure a satellite-based communications channel. The reasons for this lie as much in the realm of human behavior as they do in the realm of physics. Broadly speaking, it is not unreasonable to conclude that satellite encryption would be entirely unnecessary if every human being were perfectly trustworthy. That is to say, the desire to protect our messages from the possibility of extraterrestrial interception and decipherment notwithstanding, there would be no need to encrypt satellite communications if only those individuals entitled to send or receive a particular satellite transmission actually attempted to do so. In reality, however, human beings, organizations, and governments commonly possess competing or contradictory agendas, thus implying the need to protect the confidentiality, integrity, and availability of information transmitted via satellite.
Keeping such human behavioral concerns in mind, we can also understand the need for satellite encryption from a physical perspective. Consider, for example, a Type 1 communications satellite that has been placed into orbit above Earth’s equator. Transmissions from the satellite to the terrestrial surface (the downlink channel) would commonly be made by way of a parabolic antenna. Although such an antenna facilitates focusing the signal, the signal nevertheless disperses in a conical fashion as it departs the spacecraft and approaches the surface of the planet. The result is that the signal may be made available over a wider geographic area than would be optimally desirable for security purposes. As with terrestrial radio, in the absence of encryption, anyone within range of the signal who possesses the requisite equipment could receive the message. In this particular example, the geographic area over which the signal would be dispersed would depend both on the focal precision of the parabolic antenna, and the altitude of the satellite above Earth. These concepts are illustrated in Fig. 47.2.
Figure 47.2. Effect of altitude and focal precision on satellite signal dispersion.
Because the sender of a satellite message may have little or no control over to whom the transmission is theoretically available, protecting the message requires that its contents be encrypted. For similar reasons, extraplanetary transmissions sent between Type 2 satellites must also be protected. After all, with thousands of satellites orbiting the planet, the chances of an intersatellite communication being intercepted are quite good!
Aside from these considerations, the sensitivity of the information being transmitted must also be taken into account. Different entities possess different motivations for wanting to ensure the security of messages transmitted via satellite. An individual, for example, may want her private telephone calls or bank transaction details to be protected. An organization may likewise want to prevent its proprietary data from falling into the hands of its competition while a government may want to protect its military communications and national security secrets from being intercepted or compromised by an enemy. As is the case with terrestrial communications, the sensitivity of the data being transmitted via satellite must dictate the extent to which those data are protected. If the emerging global Information Society is to fully capitalize on the benefits of satellite-based communication, its citizens, organizations, and governments must be assured that their sensitive data are not being exposed to unacceptable risk. In light of these considerations, satellite encryption will almost certainly play a key role in the future advancement of mankind.
Sharon K. Black Attorney-at-Law, in Telecommunications Law in the Internet Age, 2002
1962—Communications Satellite Act
Also in 1962, Congress passed the Communications Satellite Act108 in which it opened commercial use of space for communications and created the Communications Satellite Corporation (Comsat). Organizationally, Comsat was a unique entity. It was a new, semiprivate corporation, half owned (50%) by the international communications carriers, including AT&T, and the other half (50%) owned by public investors.109 As a semiprivate corporation, Comsat was required to report to the Congress and the president each year.110
Congress delegated responsibility for satellite regulation to the FCC in three ways. First, Congress declared Comsat to be a common carrier and thus included satellite service in Title II, the common carrier portion of the Communications Act of 1934 regulated by the FCC. Second, the FCC’s authority over satellites derived from Title III of the 1934 Act, as amended, which gave the FCC broad power to regulate use of the radio-frequency spectrum for communications purposes.111 Third, under its enabling statute, Congress delegated to the FCC responsibility for implementing all communications law, including the Communications Satellite Act. However, to keep a monopoly from developing in the satellite industry, the FCC regulated details in satellite communications such as Earth station ownership. For example, the FCC required that Comsat never own more than 50% of each satellite Earth station, while the other 50% be owned by the remaining international carriers.
K. Giridhar, … Ronald A. Iltis, in Control and Dynamic Systems, 1996
5.1.3 LMS Algorithm for Fading Channels
Channel fading is often encountered in wireless applications such as mobile radio and satellite communication systems. To recover the data stream, the equalizer must track the channel variations over time. The LMS algorithm in the previous section is inappropriate for such fading channels since the gain μ(k) exponentially decays to zero and prevents any further adaptation. A periodic restart of the LMS adaptation process is possible, as proposed for recursive least-squares (RLS) adaptive algorithms . Instead, we propose a modified LMS adaptive scheme which can estimate and continually track the channel coefficients.
Consider the conditional measurement update in Eq. (45). By converting the covariance form update to information form, the Kalman gain term can be written as
where Pi · (k|k) is the filtered error covariance matrix. This follows directly by post-multiplying both sides of Eq. (46) with the column vector hiH(k) to obtain
and then dividing both sides by σn2.
Next, assume that the one-step transition matrix is F = I, and that the process noise covariance matrix Q = 0 in the underlying AR model in Eq. (2). Let max be the index of the data subsequence with the largest metric. An approximate update for the KF covariance matrix is given by
This update corresponds to the ordinary KF covariance equation (with known transmitted sequence) if the subsequence with the largest metric is assumed to be correct. For large values of k, it is reasonable to expect that the estimation errors become independent, and thus Pmaxk|k can be approximated by a diagonal matrix. We thus define
where the subscript jj refers to the jth diagonal entry in the matrix and |(hmax (l))j|is the modulus of the jth element of hmax(l).9 The resulting diagonal matrix
contains a set of gains which resemble the normalized LMS algorithm . These gains can be computed recursively according to
However, this update will cause the pseudo-Kalman gains to dacay to zero at the rate ≈ 1/k. To prevent this, a constant 0 < λ ≤ 1 is introduced to modify the update as follows:
The choice of λ decides the extent of influence the past gain values have on the present μj(k); it is usually referred to as a “forgetting” or finite-memory factor. For fading channels, λ is typically chosen in the range of 0.70-0.90, while for a slowly varying or time-invariant channel, the ideal choice would be λ ≈ 1.0.
Hence, the error-covariance updates are eliminated, and the entire MAP filter bank is updated with the single pseudo-Kalman diagonal gain matrix Λ(k). The only other modification involves the conditional innovations covariance update in Eq. (36). From Eqs. (65) and (68),
where we have assumed that Λ(k – 1) ≪ I and λ is sufficiently close to unity. Using this expression for Pik|k−1hiHk in Eq. (36), we obtain
Again, the notation from the previous section (for the time-invariant channel model) can be used to describe the various quantities. For example, the above innovations covariance is denoted by σi2k−1 and the measurement estimate becomes the conditional update
Stuart Ferguson, Rodney Hebels, in Computers for Librarians (Third Edition), 2003
Satellites work similarly to microwave but are used for transmission over longer distances. A communication satellite orbits about 35,000 km above the earth’s surface and rotates at an exact position and speed giving the impression that it remains over a fixed point on the earth’s surface. The satellite receives signals from stations on the earth’s surface. The signals are then amplified before being re-transmitted to the next earth station in direct line-of-sight. Satellites are used to overcome the problem that microwave dishes have with the curvature of the earth as the following diagram illustrates.
Figure 7.4. Satellite transmission
Nikolaos Ploskas, Nikolaos Samaras, in GPU Programming in MATLAB, 2016
Example: Convolutional coding
Convolutional coding is a general category of codes with great use in today’s wireless and satellite communications. In convolutional coding, the original message is not transmitted but only the parity bits calculated from it. Instead of splitting the message into blocks, there is a window that slides over the original message, so the parity bits are calculated over overlapping windows. The size of the window is called the constraint length. By increasing the constraint length, the system is less prone to bit errors.
The process of decoding is the estimation of the most probable original message transmitted (or in the case of a trellis representation is finding the most likely path). The most well-known decoding algorithm of convolutional codes over noisy links is the Viterbi decoder.
In the following example, we simulate the transmission of a binary sequence over a noisy channel and the decoding of the received signal to the original. The steps are as follows:
Calculate the convolutionally encoded data (parity bits) from the original message. We create a code generator matrix and a corresponding trellis structure description. The trellis has 1 input, 7 shift registers and 4 outputs, so the rate of the feedforward encoder is 14.
Modulate the data with the BPSK modulator in order to be transmitted.
Add white noise to the signal (noise from the transmission link).
Demodulate the signal.
Decode the signal with the Viterbi decoder.
Function convolutionalCodingCPU (filename: convolutionalCodingCPU.m) is used to illustrate the above example in MATLAB.
Initially, we measure the execution time of this function running on the CPU using 100 blocks of 50, 000 bits per block:
To execute this function on the GPU, we should perform some changes to the code. First of all, we should change all System objects to the equivalent GPU-enabled ones, for example, from comm.ConvolutionalEncoder to comm.gpu. ConvolutionalEncoder. Moreover, we should pass a gpuArray as input to that function.
Function convolutionalCodingGPU (filename: convolutionalCodingGPU.m) is used to illustrate the preceding example in MATLAB running on the GPU.
We measure the execution time of this function running on the GPU using the same data:
Hence, the convolutional coding function is running ∼8 × faster on the GPU.