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Cliff effect

The cliff effect, also known as the brick-wall effect, is a phenomenon in digital telecommunications where signal reception abruptly transitions from clear and error-free to complete loss as the signal-to-noise ratio decreases below a critical threshold.^[1] Unlike analog signals, which degrade gradually with increasing noise—resulting in progressively worsening audio or video quality—digital signals maintain high fidelity until the point of failure due to error-correcting codes and modulation techniques. This all-or-nothing behavior, often visualized as a "cliff" in performance curves, is particularly evident in broadcasting and wireless systems.^[2] The effect arises from the binary nature of digital data transmission, where forward error correction allows reconstruction of data only if errors remain below a correctable limit, typically around 1-3 decibels (dB) above the receiver's sensitivity.^[3] For example, in digital television (DTV), a signal might appear perfect at a certain distance from the transmitter but vanish entirely just beyond, impacting viewer experience in over-the-air reception.^[4] This characteristic enhances efficiency in spectrum use but poses challenges for coverage and reliability in applications like mobile networks and Wi-Fi.^[5]

Fundamentals

Definition and Phenomenon

The cliff effect, commonly referred to as the benefits cliff, is a phenomenon in public assistance programs where a small increase in a household's income leads to the sudden and complete loss of eligibility for one or more benefits, resulting in a net financial loss that can exceed the income gain.^[6] This abrupt cutoff arises from strict income eligibility thresholds in means-tested programs, such as SNAP, Medicaid, and housing subsidies, where benefits do not phase out gradually but end entirely once income surpasses a limit.^[7] In practice, families experience full benefits up to the threshold, after which they lose all support in that program, potentially facing higher effective marginal tax rates over 100%. For example, a modest raise might disqualify a family from multiple programs simultaneously, leading to reduced overall resources despite higher earnings.^[8] This all-or-nothing outcome contrasts with gradual income-based adjustments, creating strong disincentives for work or advancement.^[9] The "cliff" metaphor describes this sharp drop, where stable benefits provide security just below the threshold, but crossing it results in significant hardship, akin to falling off an edge. The effect has been documented since the 1990s in welfare reform discussions, particularly following the U.S. Personal Responsibility and Work Opportunity Reconciliation Act of 1996, which emphasized employment but highlighted unintended barriers.^[10]

Comparison to Analog Degradation

In systems with gradual phase-outs, such as some tax credits or progressive benefit reductions, assistance decreases incrementally as income rises, allowing for a form of "graceful degradation" where support remains partially available despite increasing earnings. This progressive approach enables households to retain some benefits, making the transition smoother and less punitive; for instance, earned income tax credits (EITC) phase out over a range of incomes, providing continued though reduced support.^[11] In contrast, cliff-effect programs rely on hard eligibility cutoffs without intermediate steps, ensuring full benefits up to the limit but total withdrawal thereafter due to administrative simplicity and program funding constraints. This binary structure stems from statutory income tests that do not accommodate partial eligibility, leading to complete loss once thresholds are exceeded; typical programs like TANF or certain Medicaid expansions have fixed income limits, such as 138% of the federal poverty level in some states for adult coverage as of 2025.^[12] The practical implications are significant for recipients: gradual systems provide ongoing partial support that encourages further earnings, whereas cliffs can trap families in low-wage jobs to preserve benefits, frustrating efforts toward self-sufficiency. This difference underscores a key policy trade-off, where cliffs simplify administration but at the cost of reduced work incentives compared to phased approaches' flexibility.^[7]

Underlying Mechanisms

Role of Digital Modulation

Digital modulation techniques, such as quadrature phase shift keying (QPSK), quadrature amplitude modulation (QAM), and orthogonal frequency-division multiplexing (OFDM), encode digital data by mapping bits to specific phase and amplitude shifts of a carrier wave. In QPSK, for instance, four phase shifts represent two bits per symbol, while higher-order schemes like 16-QAM or 64-QAM use a grid of amplitude and phase combinations to encode four or six bits per symbol, respectively. OFDM extends this by dividing the signal into multiple orthogonal subcarriers, each modulated independently with schemes like QPSK or QAM, enabling efficient use of bandwidth in multipath environments. These methods produce constellation diagrams, graphical representations in the complex plane where each point (symbol) corresponds to a unique bit sequence, facilitating the transmission of higher data rates over limited spectrum.^[13]^[14] The cliff effect arises prominently from the noise sensitivity inherent in these modulation schemes, as additive noise—such as Gaussian noise from thermal sources or interference—displaces symbols within the constellation diagram. Ideal symbols cluster tightly at discrete points separated by decision boundaries; however, noise introduces a probabilistic "cloud" around each point, with the cloud's spread quantified by the signal-to-noise ratio (SNR). As SNR decreases, the clouds expand, increasing the likelihood that noise pushes a symbol across a boundary, resulting in symbol errors and bit errors. Low-order modulations like QPSK, with widely spaced points, tolerate lower SNR (around 10-15 dB for acceptable error rates) due to larger minimum distances between symbols, maintaining robustness at the cost of lower throughput. In contrast, high-order modulations like 64-QAM require higher SNR (over 25 dB) for reliable detection, as densely packed points amplify the error probability from even modest noise, precipitating a rapid rise in uncorrectable errors that defines the digital cliff.^[15]^[16] This noise vulnerability directly ties to bandwidth efficiency, where higher modulation orders achieve greater spectral efficiency—transmitting more bits per hertz—by packing more information into each symbol, essential for spectrum-constrained systems. For example, transitioning from QPSK (2 bits/symbol) to 64-QAM (6 bits/symbol) triples efficiency but halves the noise margin, making the system more prone to the cliff effect under interference or fading. In OFDM, while subcarrier diversity mitigates some multipath noise, the overall constellation density across subcarriers still heightens sensitivity in high-order configurations, exacerbating sudden performance drops in noisy channels.^[16]^[17]

Error Correction and Thresholds

Forward error correction (FEC) codes, such as convolutional codes, Reed-Solomon codes, and low-density parity-check (LDPC) codes, mitigate transmission errors in digital communications by incorporating redundant data into the transmitted signal. These codes operate by encoding information bits with additional parity or check bits (in convolutional and LDPC codes) or symbols (in Reed-Solomon codes), allowing the receiver to detect and correct errors up to a certain capacity without retransmission. Convolutional codes, for instance, use shift registers and generator polynomials to produce continuous output streams with redundancy, while Reed-Solomon codes treat data as polynomials over finite fields to correct burst errors effectively, and LDPC codes employ sparse parity-check matrices for iterative decoding that approaches theoretical limits. However, these mechanisms perform reliably only when the bit error rate (BER) remains below the code's correction threshold; beyond this point, decoding fails catastrophically, leading to a sharp increase in uncorrectable errors.^[18] The cliff effect manifests as this abrupt transition, occurring near the Shannon limit—the theoretical maximum error-free transmission rate for a given signal-to-noise ratio (SNR)—or at practical SNR thresholds where the BER escalates rapidly from acceptable levels, such as 10^{-6}, to unacceptable ones like 10^{-2}. In additive white Gaussian noise (AWGN) channels, the threshold represents the minimum SNR required for the FEC decoder to converge successfully, below which error propagation overwhelms the correction capability. For example, in systems using stronger codes like LDPC, the cliff aligns closely with the Shannon capacity boundary, resulting in near-perfect reception above the threshold and complete failure below it.^[19]^[20] Mathematically, the performance is illustrated by BER versus Eb/N_0 (energy per bit to noise power spectral density ratio) curves, which exhibit a steep drop-off at the threshold, highlighting the all-or-nothing nature of FEC. For an uncoded quadrature phase-shift keying (QPSK) modulation in AWGN, the required Eb/N_0 to achieve a BER of 10^{-5} is approximately 9.6 dB, derived from the error probability formula:

P_b = Q\left(\sqrt{2 \cdot \frac{E_b}{N_0}}\right)

where Q(\cdot) is the Q-function, and solving for the value yielding P_b = 10^{-5} gives the threshold. With FEC, this shifts leftward due to coding gain, but the curve retains its sharpness.^[21] The position of this threshold is influenced by the coding gain provided by FEC, which typically ranges from 5 to 10 dB depending on the code strength and decoding method—for instance, a rate-1/2 convolutional code yields about 5.4 dB gain with soft-decision decoding at BER = 10^{-5}. However, irreducible errors from channel impairments like fading or multipath propagation can elevate the effective threshold, as these introduce error floors that FEC alone cannot fully compensate, even with substantial redundancy.^[22]

Applications in Broadcasting

Digital Television

Digital television broadcasting standards, such as ATSC in the United States and Canada, DVB-T in Europe, and ISDB-T in Japan, employ high data rates to support high-definition television (HDTV) transmission, utilizing modulation schemes like 8VSB for ATSC and OFDM for DVB-T and ISDB-T. These standards enable efficient delivery of compressed video streams but are susceptible to the cliff effect, where minor signal degradation leads to abrupt failure in decoding due to the stringent bit error rate requirements for error-free video reconstruction.^[23] In digital TV reception, the cliff effect manifests as sudden visual disruptions, including pixelation, image freezing, or complete blackout, triggered by signal fades from obstacles like buildings or atmospheric conditions such as weather.^[24] During the 2009 U.S. DTV transition, when full-power stations ceased analog broadcasts on June 12, the Federal Communications Commission (FCC) received approximately 28,000 consumer calls in the initial hours reporting reception issues, many attributed to the cliff effect causing unexpected signal loss in marginal areas.^[25] Early FCC trials in the 1990s, conducted by the Advisory Committee on Advanced Television Service, revealed that the cliff effect contributed to coverage reductions compared to analog signals in simulated and field tests, as digital reception thresholds demanded stronger, more reliable signals without graceful degradation.^[26] Reception challenges in digital TV are exacerbated by antenna type and environmental factors; indoor antennas typically provide weaker signals than outdoor models, often pushing viewers below the decoding threshold.^[27] Multipath interference, from signal reflections off structures, can raise the effective reception threshold, making robust outdoor installations and directional antennas essential for stable HDTV viewing in urban or obstructed environments.^[28]

Digital Radio

The cliff effect in digital radio manifests distinctly in audio broadcasting standards such as Digital Audio Broadcasting (DAB) in Europe, HD Radio in the United States, and Digital Radio Mondiale (DRM) internationally, where reception transitions abruptly from clear audio to silence or muting at signal-to-noise ratio (SNR) thresholds. These systems employ orthogonal frequency-division multiplexing (OFDM) for enhanced robustness against multipath interference, yet the inherent digital nature leads to an "all-or-nothing" outcome: below the threshold, forward error correction fails, resulting in uncorrectable bit errors and immediate audio dropout rather than gradual degradation like static in analog FM. For instance, in DAB (Eureka 147 standard), the use of differential quadrature phase-shift keying (DQPSK) modulation on OFDM carriers provides simpler processing suited to lower data rates for audio (around 128-192 kbps per channel), but the cliff effect still causes complete muting when the bit error rate exceeds acceptable levels, particularly impacting stereo or multichannel streams that require higher integrity.^[29]^[30]^[31] In HD Radio, which operates in-band adjacent to existing analog FM signals, the cliff effect similarly triggers at comparable SNR levels, leading to abrupt digital audio loss and a fallback to analog if available, though pure digital modes experience full muting of enhanced features like surround sound. DRM, designed for shortwave and medium-wave bands, exhibits the same threshold behavior, with robust modes (e.g., using 4-QAM) maintaining audio until SNR drops sharply, after which error concealment in codecs like AAC fails, causing silence bursts. Unlike visual artifacts in television, these audio cliffs eliminate intermediate noise but can frustrate listeners with sudden interruptions, especially in stereo modes where channel separation demands low error rates—failure affects the entire spatial audio experience without partial playback.^[32]^[33] The deployment of Eureka 147 DAB in Europe during the 1990s highlighted cliff-related challenges, as initial rollouts in urban areas like London and Oslo revealed frequent dropouts in mobile reception due to the system's sensitivity to signal fluctuations, prompting hybrid analog-digital simulcasts to ensure continuity. In vehicles, where Doppler shifts from motion exacerbate the effect, reception cliffs occur more readily despite OFDM's multipath resilience, amplifying issues in dense urban environments with variable coverage. These early experiences influenced later adaptations, such as HD Radio's blending mechanism in the U.S. since 2002, which softens the cliff by transitioning to analog audio, and DRM's international trials since 2005, which prioritize lower-power shortwave but still contend with similar thresholds in mobile scenarios. Overall, while lower bandwidth needs in digital radio (compared to television) enable simpler modulation like DQPSK, the mobile context intensifies cliff vulnerabilities, underscoring the need for robust error correction like convolutional coding and Reed-Solomon in these standards.^[30]^[34]^[35]

Applications in Wireless Communications

Mobile Cellular Networks

In mobile cellular networks, the cliff effect arises from the abrupt failure of digital signal decoding when received power drops below critical thresholds, leading to service interruptions despite adaptive mechanisms designed to counteract channel variations. Standards such as GSM, UMTS, LTE, and 5G incorporate adaptive modulation and coding (AMC) schemes that dynamically switch between robust formats like QPSK for low signal-to-noise ratio (SNR) conditions and higher-order schemes like 256-QAM for favorable channels to maintain connectivity and throughput. However, these adaptations rely on accurate channel estimation, and during rapid transitions—such as handoffs between cells or deep fades caused by multipath interference—the system may fail to adjust in time, resulting in a sharp quality drop characteristic of the cliff effect.^[36]^[37] The impacts of the cliff effect are particularly evident in both voice and data services, where sudden signal degradation can cause dropped calls in traditional circuit-switched systems or buffering delays and stalls in packet-switched VoLTE implementations. In early 3G systems, the cliff effect reduced effective coverage areas due to the stringent decoding requirements of higher modulation orders, often limiting reliable service to zones with consistently high SNR. In LTE and 5G, similar issues manifest during VoLTE sessions, where a brief deep fade can trigger error correction failures, leading to call drops or audio interruptions if the adaptive fallback to lower modulation rates is delayed by processing overhead.^[38]^[39] Mobility introduces additional challenges, as fast fading from vehicle speeds exacerbates the cliff effect by increasing the Doppler spread and raising the minimum SNR threshold needed for reliable decoding. At speeds around 60 km/h, the rapid phase shifts in multipath signals can elevate this threshold compared to static scenarios, making urban microcells particularly vulnerable due to intensified multipath reflections from buildings and vehicles. This dynamic environment shortens the coherence time of the channel, forcing more frequent AMC adjustments that, if not perfectly synchronized, amplify the risk of cliffs during motion-induced fades.^[40] The evolution to 5G NR has incorporated advanced features like beamforming to mitigate the cliff effect, especially in mmWave bands where path loss is severe. By directing narrow beams toward users, 5G achieves higher effective SNR and reduces susceptibility to fades, allowing sustained high-modulation performance over longer distances. Nonetheless, beamforming does not fully eliminate cliffs in scenarios involving blockage or rapid misalignment during high-mobility handoffs, as the reliance on line-of-sight paths can still lead to abrupt signal loss when beams fail to track channel variations.^[41]^[42]

Wi-Fi and Broadband Wireless

In Wi-Fi networks based on IEEE 802.11 standards such as 802.11a, 802.11g, 802.11n, 802.11ac, and 802.11ax, the cliff effect arises from the reliance on high-order modulation and coding schemes (MCS) that demand specific signal-to-noise ratio (SNR) thresholds for reliable operation.^[43] For instance, 802.11ax (Wi-Fi 6) employs 1024-QAM modulation in its highest MCS indices, which requires an SNR of approximately 35 dB to achieve gigabit-level throughputs, but performance plummets sharply as SNR falls below 20 dB, preventing sustained high-rate data transmission and often reducing throughput from Gbps to near zero when even lower MCS fail due to uncorrectable errors.^[44] This abrupt transition occurs because digital modulation lacks the gradual degradation seen in analog systems, with rate adaptation mechanisms struggling to downshift quickly enough in dynamic environments.^[45] In broadband wireless contexts, the cliff effect significantly constrains fixed wireless access deployments, such as those using 802.11af in TV white spaces or LTE-U in unlicensed spectrum, particularly in rural areas where non-line-of-sight propagation leads to rapid SNR degradation.^[37] These systems typically limit reliable coverage to line-of-sight distances of a few kilometers, beyond which multipath fading or obstructions cause the signal to drop below viable MCS thresholds, resulting in complete connectivity loss rather than proportional slowdown.^[46] Such limitations have been noted in rural broadband initiatives, where the digital nature of the modulation enforces strict range boundaries to avoid the all-or-nothing failure mode.^[47] Users experience the cliff effect as sudden service interruptions, such as mid-stream video buffering failures or halted file transfers, where minor environmental changes trigger packet loss rates exceeding error correction capabilities.^[48] In the 2010s, early Wi-Fi calling trials highlighted these issues, with indoor coverage exhibiting sharp cliffs that caused abrupt call drops when users moved just beyond optimal SNR zones, underscoring the challenges in maintaining voice quality over unlicensed Wi-Fi.^[48] Co-channel interference from adjacent networks in unlicensed bands intensifies the cliff effect in Wi-Fi and broadband wireless, as overlapping transmissions elevate noise floors and provoke rapid MCS downgrades or connection failures, contrasting with the controlled interference mitigation in licensed cellular environments.^[49] This unmanaged contention can halve effective throughput or induce total blackouts in dense deployments, amplifying the sensitivity of high-order modulations like those in 802.11ax.^[50]

Mitigation Techniques

Signal Processing Methods

Signal processing methods address the cliff effect by optimizing receiver and transmitter operations to enhance signal reliability, particularly in environments prone to fading and multipath propagation, thereby extending the operational range before the signal-to-noise ratio (SNR) falls below the decoding threshold. Diversity techniques mitigate abrupt SNR drops by exploiting multiple signal paths to average out fades. Antenna diversity, exemplified by multiple-input multiple-output (MIMO) configurations, combines signals from separate antennas to improve the effective SNR, typically yielding gains of 3-6 dB in Rayleigh fading channels for dual-branch systems using maximal-ratio combining.^[51] Frequency hopping similarly diversifies the channel usage over time or frequency, reducing the impact of deep fades and maintaining performance closer to the threshold.^[52] Equalization and filtering techniques counteract multipath-induced distortions that can precipitate the cliff effect. Adaptive equalizers dynamically adjust to channel variations, compensating for intersymbol interference in modulation schemes such as orthogonal frequency-division multiplexing (OFDM). Interleaving complements forward error correction (FEC) by redistributing burst errors across codewords, enabling more effective recovery and softening the transition to uncorrectable error rates near the threshold.^[53] Power control mechanisms at the transmitter dynamically adjust output levels to sustain adequate link margins. In cellular systems, this involves real-time feedback to boost power during fades or path loss, preventing SNR excursions below the required level and thereby averting sudden service outages.^[52] Early implementations of these methods in digital television (DTV) receivers during the 2000s marked significant progress. Fifth-generation ATSC 8-VSB receivers incorporated advanced adaptive equalizers with extended tap lengths, improving multipath handling and reducing cliff effect incidence in field tests under severe urban conditions, as demonstrated in comparative evaluations of reception success rates by the Advanced Television Systems Committee (ATSC) and Federal Communications Commission (FCC).^[54]

Modern Technological Advances

Recent advancements in joint source-channel coding (JSCC) have integrated deep learning techniques to simultaneously handle data compression and error protection, particularly for video transmission, thereby reducing sensitivity to the cliff effect. Traditional separate source and channel coding schemes exhibit abrupt performance drops at low signal-to-noise ratios (SNR), but deep JSCC approaches enable graceful degradation by optimizing end-to-end transmission. For instance, deep learning-based JSCC schemes introduced around 2021 employ convolutional neural networks for wireless video delivery, achieving up to 2 dB improvement in peak signal-to-noise ratio (PSNR) at low SNRs (e.g., 1 dB) compared to conventional methods, while avoiding sharp cliffs through adaptive feature extraction and reconstruction.^[55] Subsequent works in the 2020s, such as multi-scale spatial-temporal networks, have further enhanced video JSCC by incorporating temporal dependencies, yielding 10-20% better PSNR retention under fading channels. AI-enhanced receivers leveraging neural networks for symbol detection and equalization represent another key innovation in mitigating the cliff effect within 5G systems. Deep neural networks (DNNs) process received signals directly, bypassing traditional model-based assumptions that falter at low SNRs, to recover symbols more robustly. Studies around 2023 on DNN-based equalizers for 5G new radio (NR) demonstrated improved bit error rate (BER) performance by approximately 1-3 dB in low-SNR regimes (below 0 dB), enabling reliable detection in multipath environments through learned interference cancellation.^[56] These receivers, often built on architectures like graph neural networks, integrate with orthogonal frequency-division multiplexing (OFDM) demodulation, providing 15-25% better low-SNR recovery for ultra-reliable low-latency communications (URLLC) compared to linear equalizers. In broadcasting and wireless standards, post-2015 developments incorporate layered modulation and advanced forward error correction (FEC) to promote graceful degradation. The ATSC 3.0 standard, rolled out starting in 2017, employs layered division multiplexing (LDM) to overlay a robust core layer (e.g., QPSK modulation) with higher-rate enhancement layers, allowing receivers to access basic services even as SNR drops below thresholds that would fail single-layer systems.^[57] This approach mitigates the cliff effect by ensuring partial usability, with field tests showing sustained mobile reception up to 3-5 dB lower SNR than ATSC 1.0. Similarly, 5G URLLC modes utilize polar and low-density parity-check (LDPC) codes for ultra-reliable FEC, targeting packet error rates below 10^{-5} at latencies under 1 ms, which softens performance cliffs in industrial and vehicular applications by distributing redundancy efficiently.^[58] Recent standards like Wi-Fi 7 (IEEE 802.11be, finalized 2024) further mitigate cliffs through multi-link operation (MLO) for enhanced diversity and improved FEC, supporting better reliability in broadband wireless environments.^[59] As of 2025, research on semantic coding suppresses video cliffs by prioritizing meaningful content retention over pixel-level fidelity. Semantic approaches extract high-level features (e.g., objects and actions) using generative models, transmitting compressed representations that reconstruct usable video below traditional error thresholds. For example, generative feature imputing techniques in semantic communication systems achieve up to 10-15% higher semantic retention (measured by structural similarity index) at SNRs under -2 dB, compared to standard JSCC, by imputing missing elements via diffusion models.^[60] Bit-level semantic frameworks also demonstrate cliff mitigation through adaptive quantization, preserving 20% more perceptual quality in video streams during deep fades. These methods, often integrated with mixture-of-experts transformers, emphasize task-oriented reliability for next-generation broadcasting.

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Sep 12, 2018 · The antenna gain boost provided by the analog beamforming overcomes partly the impact of high pathloss in mmWave. Therefore analog ...
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Signal to Noise Ratio WiFi | SNR WiFi Table - GNS Wireless
The SNR table below shows the Signal to Noise Ratio WiFi required for each MCS index as well as the modulation, coding and data rate per stream based on channel ...
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Minimum 802.11 SNR Sensitivity | antennas and accessories
802.11ax APs will need very high SNR thresholds (~35 dB) to use 1024-QAM modulation. In the table, the individual Modulations and the signal levels and SNR.
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Understanding Wi-Fi 4/5/6/6E/7/8 (802.11 n/ac/ax/be/bn)
Wi-Fi throughput to a Wi-Fi 6 wireless client device using an 80 MHz channel will likely max out at around 900 Mbps ±100 for 2×2 MIMO and around 650 Mbps ±60 ...
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Fixed Wireless Technologies and Their Suitability for Broadband ...
Jun 11, 2025 · Fixed-wireless network coverage is adversely affected by line-of-sight obstructions (including buildings and seasonal foliage) and weather.Missing: cliff effect
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Fixed Wireless Access (FWA): Your ultimate guide - Ericsson
Fixed Wireless Access (FWA) is a wireless connection that provides broadband access to a specific location such as a home or enterprise premises.Download 2025 Handbook · Mobility Report · Download my copyMissing: cliff | Show results with:cliff
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https://www.fcc.gov/document/report-reception-digital-television-signal-0
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Co-Channel Interference - A Case Study - RHO Wireless
In practice, interference in WLAN applications usually manifests itself as reduced data-traffic throughput, less effective range, and impaired quality of ...
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Performance Evaluation of Co-Channel Interference on Wireless ...
Dec 5, 2022 · Therefore, this interference phenomenon has been recognized as an important problem as it causes network performance degradation.<|control11|><|separator|>
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[PDF] Performance Analysis of Diversity Techniques for Wireless ...
Different diversity techniques such as Maximal-Ratio Combining (MRC), Equal-Gain. Combining (EGC) and Selection Combining (SC) are described and analyzed.
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[PDF] Power Control in Wireless Cellular Networks - Princeton University
Intuitively, if these conditions are satisfied, the power of each user moves toward the SIR threshold, and it will not stop moving until the target is reached.
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(PDF) Forward error correction strategies for media streaming over ...
... cliff effect. Retraining with noisy images processed by LCT restores detection mAP degradation from 23.8% to 4.4% and segmentation mIoU degradation from ...
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[PDF] ATSC Recommended Practice: ATSC 3.0 Field Test Plan (A/326)
Feb 22, 2017 · This document presents the objectives of, and general methodology for, conducting field tests of the ATSC 3.0 over-the-air (OTA) terrestrial ...
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Ultra Reliable and Low Latency Communications - 3GPP
Jan 2, 2023 · URLLC is a major axis of enhancement of the 5G System. In Rel-16, the main improvement for URLLC is the introduction of Redundant transmission ...Missing: FEC cliff