Hubbry Logo
Communication channelCommunication channelMain
Open search
Communication channel
Community hub
Communication channel
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Communication channel
Communication channel
Communication channel
View on Wikipedia
from Wikipedia

Different types of physical transmission media supporting communication channels

A communication channel refers either to a physical transmission medium such as a wire, or to a logical connection over a multiplexed medium such as a radio channel in telecommunications and computer networking. A channel is used for information transfer of, for example, a digital bit stream, from one or several senders to one or several receivers. A channel has a certain capacity for transmitting information, often measured by its bandwidth in Hz or its data rate in bits per second.

Communicating an information signal across distance requires some form of pathway or medium. These pathways, called communication channels, use two types of media: Transmission line-based telecommunications cable (e.g. twisted-pair, coaxial, and fiber-optic cable) and broadcast (e.g. microwave, satellite, radio, and infrared).

In information theory, a channel refers to a theoretical channel model with certain error characteristics. In this more general view, a storage device is also a communication channel, which can be sent to (written) and received from (reading) and allows communication of an information signal across time.

Examples

[edit]

Examples of communications channels include:

  1. A connection between initiating and terminating communication endpoints of a telecommunication circuit.
  2. A single path provided by a transmission medium via either
  3. A path for conveying electrical or electromagnetic signals, usually distinguished from other parallel paths.
    • A data storage device which can communicate a message over time.[1]
    • The portion of a storage medium, such as a track or band, that is accessible to a given reading or writing station or head.
    • A buffer from which messages can be put and got.
  4. In a communications system, the physical or logical link that connects a data source to a data sink.
  5. A specific radio frequency, pair or band of frequencies, usually named with a letter, number, or codeword, and often allocated by international agreement, for example:
    • Marine VHF radio uses some 88 channels in the VHF band for two-way FM voice communication. Channel 16, for example, is 156.800 MHz. In the US, seven additional channels, WX1 - WX7, are allocated for weather broadcasts.
    • Television channels such as North American TV Channel 2 at 55.25 MHz, Channel 13 at 211.25 MHz. Each channel is 6 MHz wide. This was based on the bandwidth required by analog television signals. Since 2006, television broadcasting has switched to digital modulation (digital television) which uses image compression to transmit a television signal in a much smaller bandwidth, so each of these physical channels has been divided into multiple virtual channels each carrying a DTV channel.
    • Original Wi-Fi uses 13 channels in the ISM bands from 2412 MHz to 2484 MHz in 5 MHz steps.
    • The radio channel between an amateur radio repeater and an amateur radio operator uses two frequencies often 600 kHz (0.6 MHz) apart. For example, a repeater that transmits on 146.94 MHz typically listens for a ham transmitting on 146.34 MHz.

All of these communication channels share the property that they transfer information. The information is carried through the channel by a signal.

Channel models

[edit]

Mathematical models of the channel can be made to describe how the input (the transmitted signal) is mapped to the output (the received signal). There exist many types and uses of channel models specific to the field of communication. In particular, separate models are formulated to describe each layer of a communication system.

A channel can be modeled physically by trying to calculate the physical processes which modify the transmitted signal. For example, in wireless communications, the channel can be modeled by calculating the reflection from every object in the environment. A sequence of random numbers might also be added to simulate external interference or electronic noise in the receiver.

Statistically, a communication channel is usually modeled as a tuple consisting of an input alphabet, an output alphabet, and for each pair (i, o) of input and output elements, a transition probability p(i, o). Semantically, the transition probability is the probability that the symbol o is received given that i was transmitted over the channel.

Statistical and physical modeling can be combined. For example, in wireless communications, the channel is often modeled by a random attenuation (known as fading) of the transmitted signal, followed by additive noise. The attenuation term is a simplification of the underlying physical processes and captures the change in signal power over the course of the transmission. The noise in the model captures external interference or electronic noise in the receiver. If the attenuation term is complex it also describes the relative time a signal takes to get through the channel. The statistical properties of the attenuation in the model are determined by previous measurements or physical simulations.

Communication channels are also studied in discrete-alphabet modulation schemes. The mathematical model consists of a transition probability that specifies an output distribution for each possible sequence of channel inputs. In information theory, it is common to start with memoryless channels in which the output probability distribution only depends on the current channel input.

A channel model may either be digital or analog.

Digital channel models

[edit]

In a digital channel model, the transmitted message is modeled as a digital signal at a certain protocol layer. Underlying protocol layers are replaced by a simplified model. The model may reflect channel performance measures such as bit rate, bit errors, delay, delay variation, etc. Examples of digital channel models include:

Analog channel models

[edit]

In an analog channel model, the transmitted message is modeled as an analog signal. The model can be a linear or non-linear, time-continuous or time-discrete (sampled), memoryless or dynamic (resulting in burst errors), time-invariant or time-variant (also resulting in burst errors), baseband, passband (RF signal model), real-valued or complex-valued signal model. The model may reflect the following channel impairments:

Types

[edit]

Channel performance measures

[edit]

These are examples of commonly used channel capacity and performance measures:

Multi-terminal channels, with application to cellular systems

[edit]
See also: Network topology

In networks, as opposed to point-to-point communication, the communication media can be shared between multiple communication endpoints (terminals). Depending on the type of communication, different terminals can cooperate or interfere with each other. In general, any complex multi-terminal network can be considered as a combination of simplified multi-terminal channels. The following channels are the principal multi-terminal channels first introduced in the field of information theory[citation needed]:

  • A point-to-multipoint channel, also known as a broadcast medium (not to be confused with broadcasting channel): In this channel, a single sender transmits multiple messages to different destination nodes. All wireless channels except directional links can be considered as broadcasting media, but may not always provide broadcast service. The downlink of a cellular system can be considered as a point-to-multipoint channel, if only one cell is considered and inter-cell co-channel interference is neglected. However, the communication service of a phone call is unicasting.
  • Multiple access channel: In this channel, multiple senders transmit multiple possible different messages over a shared physical medium to one or several destination nodes. This requires a channel access scheme, including a media access control (MAC) protocol combined with a multiplexing scheme. This channel model has applications in the uplink of cellular networks.
  • Relay channel: In this channel, one or several intermediate nodes (called relay, repeater or gap filler nodes) cooperate with a sender to send the message to an ultimate destination node.
  • Interference channel: In this channel, two different senders transmit their data to different destination nodes. Hence, the different senders can have a possible crosstalk or co-channel interference on the signal of each other. The inter-cell interference in cellular wireless communications is an example of an interference channel. In spread-spectrum systems like 3G, interference also occurs inside the cell if non-orthogonal codes are used.
  • A unicast channel is a channel that provides a unicast service, i.e. that sends data addressed to one specific user. An established phone call is an example.
  • A broadcast channel is a channel that provides a broadcasting service, i.e. that sends data addressed to all users in the network. Cellular network examples are the paging service as well as the Multimedia Broadcast Multicast Service.
  • A multicast channel is a channel where data is addressed to a group of subscribing users. LTE examples are the physical multicast channel (PMCH) and multicast broadcast single frequency network (MBSFN).

References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Communication channel

View on Grokipedia
from Grokipedia
A communication channel is the medium or pathway that conveys signals carrying information from a transmitter to a receiver in a communication , potentially subject to and constraints on transmission rate. In information theory, as formalized by in , the channel is modeled as a probabilistic mapping between input symbols and output symbols, with its capacity defined as the maximum rate at which information can be reliably transmitted, measured in bits per second. This capacity, given by C=Wlog⁡2(1+S/N)C = W \log_2 (1 + S/N) for continuous channels with bandwidth WW, signal power SS, and power NN, sets fundamental limits on data rates over noisy media like lines or radio links. Communication channels are broadly classified into guided (wireline) and unguided () types based on whether they use a physical conduit or propagate signals through free space. Guided channels, such as twisted-pair copper wires used in systems, coaxial cables for , and for high-speed , provide dedicated paths that minimize interference but are limited by physical distance and installation costs. s, leveraging of light, achieve very high data rates over long distances with low attenuation. In contrast, unguided channels employ electromagnetic waves, including radio frequencies for and cellular networks, microwaves for links, and for short-range applications, offering mobility but susceptible to environmental fading and . These channels form the backbone of modern infrastructure, enabling everything from voice calls to global connectivity, with ongoing advancements in and beyond addressing higher capacities and lower latencies.

Fundamentals

Definition and Basic Concepts

A communication channel is defined as the medium or pathway through which information is conveyed from a transmitter to a receiver, often subject to impairments such as or that can alter the transmitted signal. In this context, the transmitter, also known as the encoder, processes the original message from an information source into a suitable signal for transmission, while the receiver, or decoder, reconstructs the message from the incoming signal at the destination. The foundational framework for understanding communication channels is provided by Claude Shannon's , which includes key components: an information source that generates the message, a transmitter that encodes it, the channel itself that carries the signal, a receiver that decodes it, a destination or sink that interprets the message, and a source that introduces perturbations. This model distinguishes between ideal channels, which are noiseless and perfectly transmit the signal without alteration, and real-world channels, where corrupts the signal, leading to potential loss of information. Signals in communication channels can be represented as continuous-time functions for analog systems, where the signal varies smoothly over time, or as discrete-time sequences for digital systems, consisting of sampled values at specific intervals. During transmission, signals experience basic effects such as propagation delay, the time required for the signal to travel from to receiver—calculated as divided by propagation speed—and , a reduction in signal due to over the medium. A basic mathematical representation of a communication channel models the output YY as a function ff of the input XX plus additive NN, expressed as Y=f(X)+NY = f(X) + N, where ff captures deterministic transformations like or delay, and NN represents random disturbances.

Historical Development

The concept of a communication channel originated in the with the advent of wired electrical transmission systems. Samuel F. B. Morse developed a electromagnetic telegraph receiver in , enabling the transmission of coded messages over wires, which marked the establishment of the first practical channels. This innovation laid the groundwork for long-distance signaling without physical transport of messages. In 1876, received a U.S. for the , introducing voice transmission over electrical wires and expanding channels to analog audio signals. The early 20th century saw the shift to wireless channels, beginning with Guglielmo Marconi's experiments in in 1895, which demonstrated the transmission of signals through the air using electromagnetic waves. This paved the way for radio channels, further advanced by the invention of the by in 1906, which provided amplification essential for long-distance telephony and . Key precursors to emerged in the 1920s, with Ralph Hartley proposing a measure of information as the number of selectable symbols in 1928, independent of meaning, and developing the sampling theorem that same year, establishing foundational limits on signal representation in channels. The formalization of the communication channel concept arrived in the information theory era with Claude Shannon's seminal 1948 paper, "," which defined the channel as a probabilistic mapping from input to output signals, incorporating as a core element. Shannon's model diagram illustrated the channel as a distinct component separate from the information source and receiver, profoundly influencing system design by emphasizing capacity limits and error correction. In this work, he introduced the , proving that reliable communication is possible at rates below the channel's capacity through appropriate encoding, despite interference. Post-1948 developments integrated these theoretical insights with practical advancements, including the formalization of modulation techniques like (AM), pioneered in the early 1900s but refined in the 1930s for broadcasting, and (FM), invented by Edwin H. Armstrong in 1933 to suppress noise in radio channels. The transition to digital channels began with precursors like the in 1969, when the first connection was established on between UCLA and Stanford, enabling packet-switched data transmission over shared networks and foreshadowing modern digital communication infrastructures.

Examples

Physical Channels

Physical channels encompass the tangible media through which signals propagate for communication, including wired, , and optical variants, each characterized by distinct mechanisms and susceptibility to degradation factors such as and dispersion. refers to the progressive weakening of the signal strength over distance due to energy absorption or scattering in the medium, while dispersion involves the spreading of signal pulses, which can distort integrity and limit effective bandwidth. Environmental factors further influence performance; for instance, affects wired channels, and atmospheric conditions like impact ones. Wired channels, such as twisted-pair copper cables, utilize pairs of insulated copper wires twisted together to minimize and , enabling reliable short-range data transmission. For example, Category 5 twisted-pair cables, commonly used in Ethernet networks, support data rates up to 100 Mbps over distances of approximately 100 meters before significant signal degradation occurs. cables, consisting of a central conductor surrounded by a insulator and metallic shielding, offer higher bandwidth capabilities suitable for applications like , with transmission rates reaching tens of Mbps and typically ranging from 7 to 27 dB per kilometer at 10 MHz frequencies, though signal loss increases with distance and frequency. (PLC) repurposes existing for data overlay, but it contends with high levels of and interference from household appliances and power fluctuations, limiting reliable throughput in noisy environments. Wireless channels rely on electromagnetic wave propagation through free space, with (RF) channels exemplifying this via air as the medium. RF systems, such as operating at 2.4 GHz or 5 GHz bands, transmit signals that can suffer from multipath fading, where reflected waves arrive out of phase, causing constructive or destructive interference and signal fluctuations. Optical wireless channels, like free-space optics (FSO), employ modulated beams for line-of-sight transmission through the atmosphere, providing high bandwidth potential but vulnerability to from fog, rain, or dust, which scatter light and reduce link reliability over distances beyond a few kilometers. Optical fiber channels transmit data via light pulses confined within or cores, offering superior performance for long-haul applications. Single-mode fibers, with a core diameter of about 9 microns, support terabits-per-second capacities through and exhibit low , typically below 0.2 dB per kilometer at 1550 nm wavelengths, enabling transcontinental links with minimal signal loss. Multimode fibers, used for shorter distances, accommodate multiple light paths but experience higher dispersion due to modal spread. The deployment of fiber optics, achieving practical low-loss transmission in the 1970s, revolutionized the global by providing the high-capacity infrastructure essential for modern data networks. Other physical media include acoustic channels, which propagate sound waves through for underwater applications like systems. These channels are band-limited and highly reverberant, with multipath effects from surface and bottom reflections causing significant signal spreading and over distances, often restricting data rates to kilobits per second in shallow waters. Such channels introduce from ambient sounds, impacting reliability in marine environments.

Mathematical Channel Examples

Mathematical channel models provide idealized abstractions for analyzing communication systems, focusing on probabilistic transitions between inputs and outputs rather than physical implementations. These models facilitate the study of fundamental limits like capacity and error rates in information theory. Noiseless channels represent perfect transmission scenarios where the output exactly matches the input. A deterministic noiseless channel follows the mapping Y=XY = X, ensuring no information loss. This is equivalent to the binary symmetric channel (BSC) with crossover probability p=0p = 0, where binary inputs X∈{0,1}X \in \{0, 1\} are received without error as Y=XY = X. Such models serve as baselines for understanding error-free communication rates. Noisy discrete channels introduce errors probabilistically, modeling imperfections like bit flips. The binary symmetric channel (BSC) with crossover probability pp (where 0<p<0.50 < p < 0.5) transmits binary symbols such that P(Y=1−X)=pP(Y = 1 - X) = p and P(Y=X)=1−pP(Y = X) = 1 - p, symmetrically affecting both inputs. This model, pivotal in early coding theory for developing error-correcting codes, captures symmetric error patterns in binary transmission. The binary erasure channel (BEC), defined with erasure probability α\alpha (where 0<α<10 < \alpha < 1), outputs the input X∈{0,1}X \in \{0, 1\} correctly with probability 1−α1 - \alpha, but erases it (outputting a distinct symbol, say "?") with probability α\alpha. Introduced as a simplified noisy channel for coding analysis, the BEC highlights scenarios where errors are detectable but information is lost. The Z-channel exemplifies asymmetric noisy discrete channels, where errors occur preferentially in one direction. In the Z-channel, input X=0X = 0 is always received as Y=0Y = 0, while X=1X = 1 is received as Y=1Y = 1 with probability 1−p1 - p and flipped to Y=0Y = 0 with probability pp (for 0<p<10 < p < 1). This memoryless model, with independent outputs given inputs, approximates channels like certain optical or magnetic storage systems prone to one-sided errors. Continuous channels extend these ideas to real-valued signals corrupted by noise. The additive white Gaussian noise (AWGN) channel models the output as Y=X+ZY = X + Z, where XX is the input signal and Z∼N(0,σ2)Z \sim \mathcal{N}(0, \sigma^2) is zero-mean Gaussian noise with variance σ2\sigma^2. As a canonical model approximating many physical channels like radio transmission under thermal noise, the AWGN facilitates derivations of capacity under power constraints. These examples are typically memoryless, meaning channel uses are independent. Channels with memory, such as finite-state channels, extend this by allowing output probabilities to depend on previous inputs via a finite set of states, modeling correlated noise in sequences. A key performance measure for these channels is the mutual information I(X;Y)I(X; Y), quantifying transmitted information. For the BSC with uniform input distribution, it simplifies to I(X;Y)=1−Hb(p),I(X; Y) = 1 - H_b(p), where Hb(p)=−plog⁡2p−(1−p)log⁡2(1−p)H_b(p) = -p \log_2 p - (1-p) \log_2 (1-p) is the binary entropy function. This expression establishes the channel's capacity as the maximum I(X;Y)I(X; Y).

Channel Models

Analog Channel Models

Analog communication channels are frequently modeled as linear time-invariant (LTI) systems, representing the channel as a linear filter that processes continuous-time, continuous-amplitude signals through convolution with its impulse response h(t)h(t). The output signal y(t)y(t) is expressed as the convolution integral of the input signal x(t)x(t) with h(t)h(t), plus additive noise n(t)n(t): y(t)=∫−∞∞x(τ)h(t−τ) dτ+n(t).y(t) = \int_{-\infty}^{\infty} x(\tau) h(t - \tau) \, d\tau + n(t). This model captures the waveform propagation effects in physical media, such as attenuation and delay, assuming the system's properties do not change over time. The impulse response h(t)h(t) fully characterizes the LTI channel, obtained by applying a Dirac delta input and observing the response. In the frequency domain, the LTI model employs the transfer function H(f)H(f), defined as the Fourier transform of h(t)h(t): H(f)=∫−∞∞h(t)e−j2πft dt,H(f) = \int_{-\infty}^{\infty} h(t) e^{-j 2 \pi f t} \, dt, with the inverse transform pairing back to the time domain. The output spectrum becomes Y(f)=X(f)H(f)+N(f)Y(f) = X(f) H(f) + N(f), where X(f)X(f) and N(f)N(f) are the Fourier transforms of the input and noise, respectively. This representation highlights bandwidth limitations, as real channels attenuate high frequencies beyond a certain cutoff, such as the Nyquist bandwidth defined by the channel's passband extent. Amplitude distortion arises when the magnitude ∣H(f)∣|H(f)| is not flat across the signal band, altering signal strength unevenly, while phase distortion occurs if the phase arg⁡(H(f))\arg(H(f)) deviates from linearity, introducing differential delays. In bandlimited channels, these distortions manifest as intersymbol interference (ISI), where signal tails from one waveform overlap with subsequent ones, degrading waveform integrity. The noise component n(t)n(t) in analog channels is commonly modeled as additive white Gaussian noise (AWGN) with a constant power spectral density (PSD) Sn(f)=N0/2S_n(f) = N_0 / 2, representing thermal noise across the channel bandwidth. Specific analog channel models illustrate these principles; for instance, the telephone voiceband channel is bandlimited to 300–3400 Hz to optimize speech transmission while minimizing bandwidth usage and noise. In contrast, the broadcast radio channel often incorporates fading due to multipath propagation, where the received signal amplitude fluctuates as y(t)=α(t)x(t)∗h(t)+n(t)y(t) = \alpha(t) x(t) * h(t) + n(t), with α(t)\alpha(t) modeling slow or fast fading envelopes, such as Rayleigh fading in non-line-of-sight environments. For passband channels operating around a carrier frequency, an equivalent baseband model simplifies analysis by shifting the spectrum to baseband using the complex envelope representation. The passband transfer function Hp(f)H_p(f) around carrier fcf_c is mapped to a lowpass equivalent Hb(f)≈Hp(fc+f)H_b(f) \approx H_p(f_c + f), reducing computational complexity while preserving distortion and noise effects in the baseband signal xb(t)x_b(t). This approach is particularly useful for modeling modulated analog signals without altering the underlying .

Digital Channel Models

Digital channel models represent communication channels in discrete-time form, obtained by sampling continuous analog signals at regular intervals according to the Nyquist-Shannon sampling theorem, which ensures faithful reconstruction if the sampling rate exceeds twice the signal's bandwidth. These models facilitate computational analysis and processing, transforming the channel into a sequence of discrete symbols processed by linear time-invariant (LTI) digital filters, whose behavior is analyzed using the . The of a discrete-time signal xx is defined as X(z)=∑n=−∞∞xz−nX(z) = \sum_{n=-\infty}^{\infty} x z^{-n}, where zz is a complex variable, enabling the representation of LTI systems as rational functions H(z)=∑k=0Mbkz−k1+∑k=1Nakz−kH(z) = \frac{\sum_{k=0}^{M} b_k z^{-k}}{1 + \sum_{k=1}^{N} a_k z^{-k}}, which simplifies stability and frequency response analysis in digital communication systems. Quantization and coding further digitize the signal, with pulse-code modulation (PCM) serving as a foundational technique invented by Alec Reeves in 1937, involving uniform sampling followed by amplitude quantization into discrete levels and binary encoding. In PCM, an analog signal is sampled at rate fsf_s, quantized to L=2bL = 2^b levels using bb bits, and coded into a binary stream, introducing quantization noise modeled as additive uniform noise with variance σq2=Δ212\sigma_q^2 = \frac{\Delta^2}{12}, where Δ\Delta is the quantization step size. Digital channels are often abstracted as discrete memoryless sources characterized by transition probabilities P(Y∣X)P(Y|X), where XX is the input symbol from alphabet X\mathcal{X} and YY is the output from Y\mathcal{Y}, forming a stochastic matrix that captures noise-induced errors without dependence on prior symbols. Error models in digital channels quantify reliability through metrics like bit error rate (BER), defined as the probability Pe=P(X^≠X)P_e = P(\hat{X} \neq X) of decoding errors, which depends on signal-to-noise ratio and modulation scheme. For channels with memory, such as those exhibiting fading, hidden Markov models (HMMs) approximate the error process, where unobserved states represent channel conditions and observations are received symbols, with transition probabilities between states capturing temporal correlations. In wireless digital channels, Rayleigh fading models the envelope of the received signal as a Rayleigh-distributed random variable due to multipath propagation without line-of-sight, leading to BER expressions like Pb=12(1−γ1+γ)P_b = \frac{1}{2} \left(1 - \sqrt{\frac{\gamma}{1 + \gamma}}\right)
Add your contribution
Related Hubs
User Avatar
No comments yet.