Wireless Channel and Access Fundamentals

Introduction and Context

A wireless communication system transfers information through an unguided electromagnetic medium. Unlike a guided cable, the wireless channel is time-varying, frequency-selective, shared by many users, and strongly influenced by the environment. Buildings, terrain, vehicles, atmospheric conditions, antenna height, mobility, and network loading all affect the received signal. Consequently, the design of a mobile system requires more than selecting a modulation scheme: it requires a consistent understanding of propagation, link budgets, fading, interference, diversity, coding, multiplexing, duplexing, and multiple access.

This chapter develops the physical-layer foundations needed to analyse mobile links. The emphasis is on models that are useful for engineering design while recognizing their assumptions and limitations. In practice, detailed channel prediction may require measurement, ray tracing, or stochastic simulation; nevertheless, analytical models remain essential for dimensioning coverage, estimating capacity, and comparing architectures.

Learning Objectives

After studying this chapter, a learner should be able to:

1. Explain the mechanisms of large-scale and small-scale wireless propagation.

2. Construct and interpret a link budget using consistent units.

3. Distinguish path loss, shadowing, multipath fading, and interference.

4. Explain diversity, coding, modulation, multiplexing, duplexing, and multiple-access methods.

5. Analyse the roles of MIMO and OFDM in modern mobile systems.

6. Use Shannon capacity and practical spectral-efficiency models critically.

7. Evaluate design trade-offs in an applied mobile-network scenario.

1. The Wireless Channel as a System Model

A useful baseband representation of a wireless link is

\[

y(t)=h(t)*x(t)+n(t)+i(t),

\]

where \(x(t)\) is the transmitted signal, \(h(t)\) is the channel impulse response, \(*\) denotes convolution, \(n(t)\) is receiver noise, and \(i(t)\) represents interference. In a narrowband flat-fading approximation, the model becomes

\[

y=h x+n+i,

\]

where \(h\) is a complex channel coefficient incorporating attenuation and phase rotation. This approximation is valid when the signal bandwidth is much smaller than the channel coherence bandwidth and when the channel is approximately constant over the symbol interval.

For a wideband multipath channel, delayed copies of the transmitted waveform arrive with different amplitudes and phases. A time-varying impulse response can be represented conceptually as

\[

h(t,\tau)=\sum_k a_k(t)e^{j\phi_k(t)}\delta(\tau-\tau_k(t)),

\]

where each path has amplitude \(a_k\), phase \(\phi_k\), and delay \(\tau_k\). The channel is therefore characterized not only by average attenuation but also by delay spread and Doppler spread.

Important channel scales include:

• Coherence time: the approximate time over which the channel remains correlated. It decreases as terminal speed and carrier frequency increase.
• Coherence bandwidth: the frequency range over which channel gains are correlated. It is inversely related to multipath delay spread.
• Delay spread: the time dispersion caused by different propagation paths.
• Doppler spread: spectral spreading caused by relative motion between transmitter, receiver, and scatterers.

A signal is approximately flat-faded if its bandwidth is smaller than the coherence bandwidth. It is frequency-selective if its bandwidth is comparable to or larger than the coherence bandwidth. A channel is commonly called slow-fading when its coherence time is much longer than a symbol duration, and fast-fading when channel changes occur over a comparable timescale.

2. Propagation Mechanisms and Large-Scale Attenuation

2.1 Reflection, diffraction, and scattering

Reflection occurs when a wave encounters a surface whose dimensions are large relative to its wavelength, such as a building wall or the ground. The reflected wave may reinforce or cancel the direct wave.

Diffraction permits energy to reach shadowed regions around edges and corners. It explains why coverage may exist behind an obstruction, although with additional loss.

Scattering occurs when rough surfaces or objects have dimensions comparable to the wavelength. Trees, street furniture, vehicles, and building irregularities can create many weak propagation paths.

These mechanisms produce both useful and harmful effects. Multipath can cause deep fades, but appropriately designed receivers can exploit independent paths through diversity and MIMO processing.

2.2 Free-space path loss

Under ideal line-of-sight propagation in free space, the received power is given by the Friis transmission equation:

\[

P_r=P_tG_tG_r\left(\frac{\lambda}{4\pi d}\right)^2,

\]

where \(P_t\) and \(P_r\) are transmit and receive powers, \(G_t\) and \(G_r\) are dimensionless antenna gains in the link direction, \(d\) is separation, and \(\lambda=c/f\) is wavelength. The equation assumes far-field operation, polarization compatibility, a clear line of sight, and free-space conditions.

Free-space path loss is

\[

L_{FS}=\left(\frac{4\pi d}{\lambda}\right)^2,

\]

or in decibels,

\[

L_{FS}(dB)=20\log_{10}\left(\frac{4\pi d}{\lambda}\right).

\]

When \(d\) is in kilometres and \(f\) is in megahertz,

\[

L_{FS}(dB)=32.44+20\log_{10}(d_{km})+20\log_{10}(f_{MHz}),

\]

with minor variation in the constant depending on rounding.

An important interpretation is that increasing frequency increases free-space loss for fixed physical distance and fixed antenna gain. However, practical high-frequency systems may use larger or electronically steered antenna arrays, and the relationship between frequency and coverage is therefore not determined by free-space loss alone.

2.3 Log-distance and shadowing models

In terrestrial environments, the log-distance model is widely used:

\[

PL(d)=PL(d_0)+10n\log_{10}\left(\frac{d}{d_0}\right)+X_\sigma.

\]

Here, \(PL(d_0)\) is the path loss at reference distance \(d_0\), \(n\) is the path-loss exponent, and \(X_\sigma\) is a zero-mean Gaussian random variable in dB with standard deviation \(\sigma\), representing log-normal shadowing. The model is empirical. It should be calibrated for a particular frequency, antenna configuration, environment, and distance range.

Typical qualitative values of \(n\) are near 2 in unobstructed free space, approximately 2.7–3.5 in many urban macrocell environments, and higher in heavily obstructed indoor or non-line-of-sight settings. These are indicative ranges rather than universal constants.

Shadowing differs from multipath fading. Shadowing varies over distances associated with large objects and terrain, whereas multipath fading can change over a fraction of a wavelength as the terminal moves.

3. Link Budgets and Noise-Limited Performance

A link budget accounts for all major gains and losses. In decibel form,

\[

P_r(dBm)=P_t(dBm)+G_t(dBi)+G_r(dBi)-L_p(dB)-L_c(dB)-L_m(dB),

\]

where \(L_p\) is propagation loss, \(L_c\) includes feeder and connector losses, and \(L_m\) represents other implementation or margin losses. Gains and losses must be expressed consistently; dBi refers to antenna gain relative to an isotropic radiator, while dBm refers to power relative to 1 mW.

Thermal noise power is

\[

N=kTB,

\]

where \(k=1.380649\times10^{-23}\) J/K is Boltzmann’s constant, \(T\) is receiver noise temperature in kelvin, and \(B\) is bandwidth in hertz. In practical receiver calculations,

\[

N(dBm)\approx -174+10\log_{10}(B_{Hz})+NF,

\]

where \(-174\) dBm/Hz is the approximate thermal noise density at 290 K and \(NF\) is receiver noise figure in dB. The formula assumes the receiver noise figure is represented as a power penalty relative to an ideal 290 K reference.

The received signal-to-noise ratio is

\[

SNR(dB)=P_r(dBm)-N(dBm).

\]

If interference is significant, signal-to-interference-plus-noise ratio is more appropriate:

\[

SINR=\frac{P_s}{P_i+N},

\]

where powers are linear, not decibel, quantities. Decibel values cannot be directly added in this expression unless first converted to linear units.

Worked Example: A Basic Link Budget

Suppose a transmitter radiates \(P_t=23\) dBm at 2 GHz. The transmit and receive antenna gains are 8 dBi and 0 dBi, respectively. The distance is 1 km, and cable and implementation losses total 3 dB. The receiver bandwidth is 10 MHz with a 5 dB noise figure.

The free-space loss is approximately

\[

L_{FS}=32.44+20\log_{10}(1)+20\log_{10}(2000)\approx 98.46\text{ dB}.

\]

Thus,

\[

P_r=23+8+0-98.46-3=-70.46\text{ dBm}.

\]

Noise power is

\[

N=-174+10\log_{10}(10^7)+5=-99\text{ dBm}.

\]

Therefore, the free-space SNR is approximately

\[

SNR=-70.46-(-99)=28.54\text{ dB}.

\]

This result is optimistic if the path is obstructed or multipath fading is severe. A design would normally include shadowing, fading, penetration, and implementation margins.

4. Fading and Interference

4.1 Small-scale fading distributions

When a dominant line-of-sight component is absent and many scattered components add, the envelope is often modelled by a Rayleigh distribution. If a strong direct component exists, a Rician model is commonly used. The Rician K-factor is the ratio of dominant-component power to diffuse-component power. A large K-factor indicates a relatively strong direct component.

These distributions are statistical approximations, not physical laws. Their validity depends on propagation geometry, antenna patterns, frequency, and measurement scale. Other models, including Nakagami-m and clustered delay-line models, may better represent particular environments.

Fading can be:

• Fast or slow, according to the rate of channel variation.
• Flat or frequency-selective, according to the relation between signal bandwidth and coherence bandwidth.
• Independent or correlated, according to the similarity of fading across antennas, frequencies, or time intervals.

4.2 Interference

Interference is often the dominant limitation in cellular networks. It may be co-channel, adjacent-channel, intersymbol, inter-cell, or cross-link interference. Increasing transmit power does not necessarily improve a network if it raises interference for other users. This distinguishes interference-limited systems from noise-limited systems.

Interference management includes frequency planning, sectorization, power control, scheduling, beamforming, coordinated multipoint operation, interference cancellation, and resource partitioning. There is a fundamental trade-off between frequency reuse and interference: aggressive reuse increases available resources but can reduce SINR.

4.3 Diversity

Diversity combats fading by providing multiple independently or partially independently faded replicas of information. The receiver combines these replicas so that a deep fade on one branch is less likely to affect all branches.

Major forms include:

• Time diversity: coding or retransmission across separated time intervals.
• Frequency diversity: transmission over frequencies separated by more than the coherence bandwidth.
• Spatial diversity: multiple antennas separated sufficiently to reduce fading correlation.
• Polarization diversity: antennas with different polarizations.
• Path diversity: exploitation of resolvable multipath components.

Selection combining chooses the branch with the highest instantaneous quality. Maximal-ratio combining weights each branch according to its channel quality and phase; under ideal conditions it maximizes output SNR for additive white Gaussian noise. Diversity improves reliability but may require extra antennas, bandwidth, delay, radio-frequency chains, or signal-processing complexity.

5. Modulation, Coding, and Link Adaptation

Modulation maps digital symbols to waveforms. BPSK and QPSK provide robust low-order constellations; 16-QAM, 64-QAM, and higher-order QAM convey more bits per symbol but require higher SINR for a target error rate. In an ideal uncoded system, the number of bits per symbol for M-ary modulation is \(\log_2 M\), although practical spectral efficiency is lower because of coding, pilots, guard intervals, control signals, and retransmissions.

Channel coding adds structured redundancy to detect and correct errors. Convolutional, turbo, low-density parity-check (LDPC), and polar codes have been important in cellular systems. The code rate is

\[

R_c=\frac{k}{n},

\]

where \(k\) information bits are represented by \(n\) coded bits. Lower code rates generally improve error correction at the cost of throughput.

Link adaptation selects modulation and coding according to channel conditions. Hybrid automatic repeat request (HARQ) combines forward error correction with retransmission, often using incremental redundancy or soft combining. Adaptation must account for channel-estimation errors, feedback delay, mobility, and fairness among users.

The Shannon capacity of an ideal complex AWGN channel is

\[

C=B\log_2(1+SNR),

\]

where \(C\) is capacity in bit/s, \(B\) is bandwidth in hertz, and SNR is linear. This is an asymptotic upper bound under the stated channel model; it is not the guaranteed throughput of a practical link.

6. Multiplexing and Duplexing

Multiplexing allows multiple data streams or logical channels to share a transmission resource. Frequency-division multiplexing separates users or channels in frequency; time-division multiplexing separates them in time; code-division multiplexing separates them using spreading sequences or codes; and spatial multiplexing separates streams using multiple antennas.

Frequency-division duplexing (FDD) uses separate frequency bands for uplink and downlink, generally requiring duplex filters and paired spectrum. It supports simultaneous transmission and reception but needs paired spectrum.

Time-division duplexing (TDD) uses the same frequency band at different times for uplink and downlink. It offers flexible uplink/downlink resource allocation and can support channel reciprocity for beamforming, but requires synchronization and guard periods to handle propagation delay and switching.

Duplexing choice is influenced by spectrum availability, traffic asymmetry, latency, hardware complexity, regulatory conditions, and interference coordination.

7. Multiple-Access Methods

Multiple access determines how independent users share network resources.

FDMA

Frequency-division multiple access assigns distinct frequency channels. It is conceptually simple but requires guard bands and careful frequency planning.

TDMA

Time-division multiple access assigns time slots. It improves frequency reuse compared with fixed frequency channels but requires timing synchronization and guard intervals.

CDMA

Code-division multiple access allows users to overlap in time and frequency using spreading codes. Capacity is interference-limited and depends on power control, code properties, multipath processing, and the number of active users.

OFDMA

Orthogonal frequency-division multiple access assigns groups of orthogonal subcarriers or resource blocks to users. It supports frequency-selective scheduling and flexible allocation. Orthogonality is lost when synchronization errors, Doppler, phase noise, or nonlinear distortion become significant.

Non-orthogonal multiple access

NOMA permits controlled signal superposition and uses successive interference cancellation or related receivers. Its gains depend strongly on channel disparities, receiver complexity, power allocation, imperfect cancellation, and scheduling. It should not be assumed to outperform orthogonal access in every load or channel condition.

8. OFDM: Principle and Engineering Trade-offs

Orthogonal frequency-division multiplexing divides a wideband channel into many closely spaced orthogonal subcarriers. The transmitter commonly uses an inverse fast Fourier transform (IFFT), inserts a cyclic prefix, and performs digital-to-analogue conversion. The receiver removes the prefix, applies an FFT, estimates the channel, and equalizes each subcarrier.

If the useful OFDM symbol duration is \(T_u\), subcarrier spacing is selected as

\[

\Delta f=\frac{1}{T_u},

\]

which establishes ideal orthogonality. A cyclic prefix of duration \(T_{CP}\) should exceed the relevant channel delay spread to prevent inter-symbol interference, subject to a practical margin. The total symbol duration is \(T_u+T_{CP}\), so the cyclic prefix reduces spectral efficiency by the factor

\[

\eta_{CP}=\frac{T_u}{T_u+T_{CP}},

\]

before other overheads are included.

OFDM converts a frequency-selective wideband channel into many approximately flat subchannels, simplifying equalization. Its disadvantages include high peak-to-average power ratio, sensitivity to frequency offset and phase noise, and overhead from pilots and cyclic prefixes. Clipping or nonlinear amplification used to control peak power can introduce distortion and out-of-band emissions.

9. MIMO and Spatial Processing

A narrowband MIMO system can be written as

\[

\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n},

\]

where \(\mathbf{x}\) is the transmitted vector, \(\mathbf{y}\) is the received vector, \(\mathbf{H}\) is the channel matrix, and \(\mathbf{n}\) is noise. MIMO provides three related benefits:

• Spatial diversity: improved reliability through multiple independently faded observations.
• Spatial multiplexing: multiple streams increase throughput when the channel has sufficient rank and adequate SINR.
• Beamforming: coherent spatial weighting concentrates energy and suppresses interference.

For a channel with perfect receiver channel knowledge and equal power allocation, an ideal capacity expression is

\[

C=\log_2\det\left(\mathbf{I}+\frac{\rho}{N_t}\mathbf{H}\mathbf{H}^{H}\right),

\]

in bit/s/Hz, where \(\rho\) is total receive SNR, \(N_t\) is the number of transmit antennas, \(\mathbf{H}^{H}\) is the conjugate transpose, and \(\mathbf{I}\) is an identity matrix of suitable dimension. This model assumes a flat-fading channel, Gaussian signalling, perfect channel knowledge at the receiver, and the stated equal-power policy. Correlation, antenna coupling, channel-estimation error, limited feedback, and hardware impairments reduce practical gains.

MIMO performance is not determined by antenna count alone. Physical spacing, polarization, scattering richness, user geometry, calibration, and propagation correlation are crucial. Massive MIMO extends these ideas with many base-station antennas and coordinated spatial processing, but introduces pilot contamination, reciprocity calibration, computational, energy, and deployment challenges.

10. Capacity, Coverage, and System-Level Trade-offs

Coverage and capacity are related but distinct objectives. A high-gain, low-band deployment may provide broad coverage but limited bandwidth. A high-frequency small-cell deployment may provide high capacity over short distances but require dense infrastructure and careful blockage management.

The Shannon expression shows that capacity grows linearly with bandwidth but only logarithmically with SNR. Consequently, spectrum expansion, spatial reuse, and MIMO can be more efficient than simply increasing transmit power. At low SNR, increasing power can be valuable; at high SNR, additional power produces diminishing returns.

Network planning must consider cell-edge throughput, percentile reliability, latency, energy efficiency, fairness, and aggregate capacity rather than only peak data rate. A link with high instantaneous throughput may still provide poor user experience if scheduling is unfair or outages are frequent.

11. Illustrative Case: Rural Broadband Link Design

Background

Consider an illustrative rural fixed-wireless service connecting a base station to households up to 5 km away at 3.5 GHz. The operator seeks reliable 50 Mbit/s service at the cell edge while minimizing tower and spectrum costs.

Problem

A free-space estimate alone may overstate coverage because terrain, foliage, shadowing, and rain or equipment margins can reduce received SINR. The system must also share spectrum with neighbouring cells.

Analysis

The design team first constructs a link budget using the log-distance model calibrated from drive-test measurements. It adds antenna gains, feeder losses, receiver noise figure, penetration loss where applicable, and a shadowing margin. Because the service is wideband, the team evaluates frequency selectivity and selects OFDM with a cyclic prefix longer than the measured dominant delay spread.

At the cell edge, the required modulation and coding scheme is selected from measured block-error-rate curves rather than from Shannon capacity alone. A lower-order modulation with a lower code rate is used during fades. At favourable locations, higher-order QAM and spatial multiplexing increase throughput.

Inter-cell interference is analysed through SINR maps. Sector antennas, coordinated scheduling, and power control are considered. The operator chooses TDD because unpaired spectrum is available and downlink traffic is greater than uplink traffic. Guard periods are included to accommodate propagation delay and synchronization.

Approach and outcome

The resulting design uses two or more spatially separated antenna branches, adaptive modulation and coding, HARQ, and an explicit cell-edge reliability target. Coverage is not declared adequate merely because the median received power is high; the design is accepted only when the required throughput and outage probability are met at the specified percentile. Field measurements are then used to recalibrate the propagation model.

Learning points

1. Link budgets must include uncertainty margins, not only nominal path loss.

2. Coverage, throughput, and reliability require different metrics.

3. OFDM and adaptive coding address channel variation but introduce overhead.

4. Interference management can be more important than additional transmit power.

5. Measurement-based validation is essential for deployment decisions.

12. Challenges, Governance, and Best Practices

Wireless design is subject to national spectrum regulation, licensing conditions, emission masks, exposure requirements, equipment certification, and coexistence rules. Engineers must respect power limits, occupied bandwidth, out-of-band emissions, and electromagnetic compatibility requirements. Privacy and security also matter: channel measurements, location data, and user traffic may be sensitive.

Best practices include:

• State the propagation and channel assumptions before using a formula.
• Keep linear and decibel quantities distinct.
• Include antenna, feeder, body, penetration, shadowing, and implementation losses where relevant.
• Validate stochastic models against measurements.
• Report percentile reliability, outage probability, and SINR distributions rather than only averages.
• Distinguish theoretical capacity from coded throughput.
• Test under mobility, blockage, interference, and hardware impairments.
• Evaluate energy efficiency and fairness alongside peak rate.
• Use AI or machine learning only with representative data, uncertainty assessment, explainable performance criteria, and protection against biased or unsafe automated decisions.

Contemporary Developments

Current wireless research combines higher-frequency operation, reconfigurable intelligent surfaces, cell-free and distributed MIMO, integrated sensing and communication, AI-assisted radio resource management, and energy-aware network control. These developments do not eliminate classical channel limitations. They increase the importance of accurate channel estimation, synchronization, hardware linearity, propagation measurement, and cross-layer optimization.

Chapter Summary

Wireless channels are shaped by reflection, diffraction, scattering, distance-dependent path loss, shadowing, multipath, Doppler, and interference. Free-space and log-distance models support initial planning, while measurements and site-specific models are required for reliable deployment. A link budget combines transmit power, antenna gains, propagation losses, receiver noise, and implementation margins; SNR must be replaced by SINR when interference is material.

Fading is mitigated through time, frequency, spatial, polarization, and path diversity. Modulation controls the symbol constellation, coding adds error-correction redundancy, and link adaptation selects an operating point appropriate to channel conditions. FDD and TDD provide alternative duplexing structures, while FDMA, TDMA, CDMA, OFDMA, and NOMA implement different resource-sharing strategies.

OFDM converts a frequency-selective channel into approximately flat subchannels but incurs cyclic-prefix overhead and peak-power sensitivity. MIMO provides diversity, spatial multiplexing, and beamforming; its gains depend on channel rank, correlation, estimation, and hardware. Shannon capacity is a useful upper bound, not a direct prediction of practical throughput. Sound engineering therefore combines analytical models, uncertainty margins, interference analysis, field measurements, regulatory compliance, and reliability-oriented performance metrics.

Key Terms

Path loss: Reduction in average received power with propagation distance and environmental effects.

Shadowing: Large-scale random variation in received power caused by obstructions and terrain.

Multipath: Arrival of multiple delayed replicas of a transmitted signal.

Fading: Variation in channel amplitude and phase caused by propagation and motion.

Doppler spread: Spectral spreading caused by relative motion.

Coherence time: Approximate time interval over which the channel remains correlated.

Coherence bandwidth: Frequency interval over which channel responses remain correlated.

Link budget: Accounting of gains and losses from transmitter to receiver.

Noise figure: Receiver noise degradation relative to an ideal reference.

SINR: Signal power divided by interference-plus-noise power.

Diversity: Use of multiple independently faded observations to improve reliability.

Modulation: Mapping of information symbols to physical waveforms.

Code rate: Ratio of information bits to transmitted coded bits.

Duplexing: Separation of uplink and downlink transmissions.

Multiple access: Method by which users share radio resources.

OFDM: Multicarrier transmission using orthogonal subcarriers.

Cyclic prefix: Repeated symbol portion used to reduce inter-symbol interference in OFDM.

MIMO: Multiple-input multiple-output antenna transmission.

Beamforming: Spatial weighting that directs energy or suppresses interference.

Shannon capacity: Theoretical maximum reliable rate for a specified channel model.

Review Questions

Short Answer Questions

1. Distinguish path loss, shadowing, and small-scale fading.

2. State the assumptions behind the Friis transmission equation.

3. Why is SINR more useful than SNR in an interference-limited cellular network?

4. What is the purpose of a cyclic prefix in OFDM?

5. Identify the three principal benefits of MIMO.

Long Answer Questions

1. Explain the major wireless propagation mechanisms and relate them to large-scale and small-scale channel models.

2. Derive and interpret a complete decibel link budget, including receiver noise and implementation margins.

3. Compare FDD and TDD with respect to spectrum, synchronization, traffic asymmetry, and interference.

4. Explain how modulation, channel coding, HARQ, and link adaptation jointly determine practical throughput.

5. Discuss OFDM and MIMO as complementary techniques for frequency-selective mobile channels, including their limitations.

Analytical / Application Questions

1. A 2 GHz link has 10 MHz bandwidth, 23 dBm transmit power, 8 dBi transmit gain, 0 dBi receive gain, 3 dB miscellaneous loss, and 5 dB receiver noise figure. At 1 km free-space distance, calculate received power, noise power, and SNR using the models in this chapter. State why the result may be optimistic.

2. A channel has measured delay spread of 4 microseconds. Explain how cyclic-prefix duration and subcarrier spacing should be selected, and identify the resulting trade-off in spectral efficiency and Doppler robustness.

3. A dense cellular network experiences high average received power but poor cell-edge throughput. Diagnose at least four possible causes and propose a measurement-based mitigation plan.

Multiple Choice Questions with Answers

Question 1

Which phenomenon is primarily represented by the shadowing term in the log-distance path-loss model?

A. Large-scale variation caused by terrain and obstructions

B. Symbol timing error within one OFDM symbol

C. Thermal noise generated by the receiver

D. Frequency spacing between adjacent subcarriers

Correct Answer: A. Large-scale variation caused by terrain and obstructions

Question 2

For a 10 MHz receiver bandwidth and 5 dB noise figure, using -174 dBm/Hz thermal noise density, what is the approximate noise power?

A. -174 dBm

B. -99 dBm

C. -70 dBm

D. -45 dBm

Correct Answer: B. -99 dBm

Question 3

Which condition best supports the use of a flat-fading channel approximation?

A. Signal bandwidth is much greater than coherence bandwidth

B. Signal bandwidth is much smaller than coherence bandwidth

C. The channel has no thermal noise

D. The transmitter uses only QAM

Correct Answer: B. Signal bandwidth is much smaller than coherence bandwidth

Question 4

What is the principal purpose of a cyclic prefix in OFDM?

A. Increase antenna gain

B. Remove all additive noise

C. Reduce inter-symbol interference caused by channel delay spread

D. Convert FDD into TDD

Correct Answer: C. Reduce inter-symbol interference caused by channel delay spread

Question 5

Which statement about Shannon capacity is correct?

A. It is the guaranteed throughput of every practical coded system

B. It applies only to optical-fibre channels

C. It is an upper bound under a specified channel model

D. It is independent of bandwidth

Correct Answer: C. It is an upper bound under a specified channel model

Question 6

Which MIMO benefit sends multiple independent data streams when the channel has sufficient rank?

A. Spatial multiplexing

B. Shadowing compensation

C. Thermal-noise generation

D. Frequency hopping only

Correct Answer: A. Spatial multiplexing

Question 7

In a link budget, which quantities can be directly added or subtracted in decibel form?

A. Power ratios expressed in dB and gains or losses expressed in dB

B. Linear powers expressed in watts without conversion

C. SINR values expressed as linear ratios

D. Probabilities and antenna gains regardless of units

Correct Answer: A. Power ratios expressed in dB and gains or losses expressed in dB

Question 8

Which duplexing method uses the same frequency band at different times for uplink and downlink?

A. FDD

B. TDD

C. FDMA only

D. Polarization-division duplexing

Correct Answer: B. TDD

Question 9

What is a principal disadvantage of OFDM?

A. It cannot operate over frequency-selective channels

B. It has high peak-to-average power ratio

C. It cannot use channel coding

D. It requires no synchronization

Correct Answer: B. It has high peak-to-average power ratio

Question 10

Maximal-ratio combining is designed primarily to do what?

A. Combine branches using channel-aware weights to maximize output SNR

B. Assign different users permanent frequency bands

C. Eliminate the need for antennas

D. Increase path loss deliberately

Correct Answer: A. Combine branches using channel-aware weights to maximize output SNR