Advanced Spectrum Efficiency techniques in 5G NR
- Venkateshu
- Sep 11
- 7 min read
Introduction
Spectral Efficiency
Spectral efficiency is the net data rate per unit bandwidth, usually measured in bit/s/Hz; it reflects how many useful bits can be carried per second for each hertz of spectrum, excluding physical-layer overheads like pilots and FEC redundancy.
Link spectral efficiency = net throughput divided by occupied bandwidth, expressed in bit/s/Hz; it can also be viewed as bits per channel use when normalized by symbol rate instead of hertz.
Example, Suppose a 5G NR downlink delivers a net 100 Mbps over a 20 MHz channel; the link spectral efficiency is 100 Mb/s÷20 MHz=5100 Mb/s÷20 MHz=5 bit/s/Hz, assuming overhead already removed from the net rate.
If multi-user or multi-layer MIMO doubles useful streams on the same 20 MHz (with adequate SINR and low inter-stream interference), the net throughput might rise to 200 Mbps, giving 200/20=10200/20=10 bit/s/Hz;
Throughput fundamentals
NR PHY throughput scales with PRBs, modulation/code rate, symbols per second, and number of layers: Throughput ∝ NPRB × 12 × Qm × R × Nsym/sec ×Nlayers, with TDD/FDD overheads deducted per TS 38.306-style formulations. Increasing channel bandwidth increases NPRB nearly linearly within a numerology, while adding spatial layers increases N layers if the channel supports low inter-layer interference and sufficient SNR for higher MCS.
Shannon perspective
Per Shannon, capacity grows with both bandwidth B and log2(1+SNR); MIMO adds spatial degrees of freedom that, in rich scattering, can increase capacity roughly linearly with the minimum of Tx/Rx antennas (rank), but only when channels are sufficiently independent. Massive MIMO and beamforming improve SNR and enable higher spatial multiplexing, yet environmental constraints (LOS/NLOS, correlation) and device form factors limit realized rank in FR1 handhelds compared to mmWave arrays.
Spectral efficiency (bits/s/Hz) is most directly increased by higher-order modulation and more spatial streams via MIMO; increasing raw bandwidth boosts throughput but not SE by definition, though it can indirectly raise achievable MCS by improving implementation and scheduling efficiency.
Bandwidth scaling
For a given SCS, PRBs scale with bandwidth (e.g., 273 PRBs for 100 MHz, μ=1), so peak data rate rises roughly linearly when moving from, say, 40 to 100 MHz, assuming similar overhead and MCS. An example with 100 MHz FR1, μ=1, 256-QAM, code rate ~0.925, 4 layers, and DL overhead 0.14 yields about 2.34 Gbps, illustrating the linear effect of PRB count with bandwidth. Carrier aggregation extends this further by summing across up to 16 CCs in NR, making bandwidth the most deterministic lever for capacity when spectrum is available.
Impact: Linear throughput gain via more PRBs; SE in bit/s/Hz stays roughly constant if MCS and rank are unchanged; indirect SE gains occur when wider/cleaner spectrum lifts SINR or reduces overhead per Hz.
Constraints: Spectrum availability/licensing, RF front‑end and fronthaul capacity, and potential PA linearity/EVM challenges at wider instantaneous bandwidths that can limit achievable MCS in practice.
When to prefer: When spectrum is available and predictable user‑rate increases are required without relying on channel rank or very high SINR scenarios.
Spatial multiplexing scaling
Spatial multiplexing increases per-RB bits by adding independent layers, scaling throughput approximately with the number of layers when channels between antennas are sufficiently uncorrelated and SNR allows the target MCS per layer. In FR1, practical DL layer counts are often 2–4 for SU-MIMO at the UE, with higher effective layers from gNB massive MIMO via MU-MIMO across UEs to raise sector capacity rather than single-UE peak rates. Gains depend on channel rank, UE antenna design, correlation, pairing (for MU-MIMO), and beamforming quality, so they are less predictable than pure bandwidth scaling.
Impact: Direct SE multiplier with rank rr if streams are sufficiently independent; massive/MU‑MIMO can raise sector SE several‑fold by reusing the same Hz across users, given accurate CSI and beam orthogonality.
Constraints: Channel rank/correlation, UE antenna geometry, calibration and CSI overhead, digital/analog beamforming complexity, and energy/circuit power that grows with RF chains and processing.
Risks: Diminishing returns if inter‑layer interference rises or CSI degrades under mobility; scheduler must pair users with complementary channels to sustain MU‑MIMO gains.
When to prefer: Spectrum‑constrained scenarios seeking higher SE/cell capacity, especially with many users and good beam separability.
Modulation and coding
Moving from 64‑QAM to 256‑QAM and to 1024‑QAM increases bits per symbol and hence SE at a given bandwidth/stream, but requires tight EVM/SINR; recent demonstrations show about 20% downlink speed and SE gain when 1024‑QAM replaces 256‑QAM under favorable conditions. In short, higher‑order QAM may deliver smaller net SE gains if retransmissions rise or device/network EVM limits are hit, so benefits materialize mainly in high‑SINR, low‑mobility scenarios.
Impact: Per‑stream SE increase by moving from 64‑QAM to 256‑QAM to 1024‑QAM; controlled demos show ~20% downlink gain moving 256→1024‑QAM in favorable radio conditions.
Constraints: Tight EVM/SINR requirements; 256‑QAM typically needs EVM ≈3.5% and 1024‑QAM demands even stricter limits with SNR often ≥30–35 dB, stressing RF linearity, phase noise, and device calibration.
Risks: If SNR margins are thin, retransmissions and fallback MCS erase gains; higher PAPR and PA back‑off can reduce energy efficiency at the cell edge.
When to prefer: High‑SINR, low‑mobility, short‑range deployments where RF quality supports tight EVM and the network can keep users in top MCS reliably.
Advanced Techniques for improving spectral efficiency
Beyond adding more layers and bandwidth, current work to raise spectral efficiency focuses on smarter multiplexing, tighter interference coordination, higher-order modulation and link adaptation, advanced multi-point/cell-free architectures, and AI-driven scheduling/resource allocation where gains outweigh complexity.
1. Higher Order Modulation and AI driven Link adaptation
Field studies show spectral efficiency largely tracks achievable MCS under stronger signal conditions, indicating that improving SINR to unlock higher MCS is a direct lever for SE gains in practice.
Conditions to realize gains
SINR/EVM constraints: 256‑QAM already needs tight EVM and high SINR; 1024‑QAM requires even stricter linearity and phase‑noise performance, so it is viable mainly in strong‑signal, low‑mobility scenarios such as small cells or close‑range mid‑band with good beams.
Link adaptation quality:
Accurate CQI/CSI and responsive AMC/OLLA avoid over‑ or under‑selecting MCS;
Outer Loop Link Adaptation (OLLA) is a feedback-driven rate control that adjusts a per-UE SINR/MCS offset so that the packet/block error rate converges to a target (e.g., 10%); it nudges the selected MCS up after successful decodes and down after failures, riding on top of CQI-based inner-loop estimation to keep reliability and throughput balanced.
OLLA adds an offset Δ to that anchor to compensate for model errors, implementation impairments, delay, and fast fading so the observed BLER hits the configured target.
RL‑based link adaptation has shown 1.6–2.5% average throughput gains mid‑cell and 10–17% at cell edge versus classic OLLA by better tracking channel dynamics, which translates to more time spent in higher MCS where feasible.
RL agents (e.g., DQN/PPO/DDPG) learn a policy mapping observed channel/traffic states to SINR corrections or direct MCS choices to maximize long-term throughput subject to BLER constraints, adapting faster to nonstationary conditions than hand-tuned OLLA.
2.Coordinated multi-point and cell-free massive MIMO
CoMP coordinates multiple transmission points to turn inter-cell interference into useful signal through techniques like Joint Transmission(JT), Dynamic Point Selection(DPS), and Coordinated Scheduling/Beamforming(CS/CB), while cell-free massive MIMO replaces cells with many distributed APs jointly serving all users via a central processor; both demand tight CSI exchange, fronthaul/backhaul capacity, time/frequency synchronization, and coordination-aware scheduling/precoding to deliver SINR and spectral-efficiency gains, especially at the edge.
These architectures rely on precise CSI sharing and synchronization; when executed well, they enable higher MCS and effective multiplexing across users without extra spectrum.
When to use which
CoMP fits incremental upgrades to existing macro/small cells where limited clusters and Xn coordination are feasible; DPS/CS are transport light, JT needs more stringent sync and data sharing.
Cell free massive MIMO targets greenfield or dense indoor/outdoor hotspots needing uniform QoS; requires careful fronthaul and clustering but can outperform cell based architectures in edge uniformity.
3. Beamforming and multi-user scheduling
Fine-grained digital/hybrid beamforming with MU-MIMO increases spatial reuse across users, improving sector spectral efficiency via better beam orthogonality and rank-aware scheduling.
Scheduler strategies that pair users with complementary channels and allocate RBs/beams jointly improve per-RB utility, especially in dense deployments with many resolvable beams.
4. Interference management and topology
Dense deployments with more beams and tighter power control elevate SINR distributions, enabling higher MCS usage and thus better spectral efficiency per Hz per stream in the field.
Techniques include dynamic TDD coordination, fractional frequency reuse, and intelligent muting/blanking to reduce harmful overlap and free higher-order modulations.
5. Numerology, waveform, and control overhead
Context-aware numerology selection and mini-slot/symbol-level scheduling can improve SE by aligning slot durations with channel coherence and traffic bursts, reducing wasted REs on control/CP.
Context-aware numerology selection dynamically picks the subcarrier spacing μ, CP, slot/minislot length, and guard allocation based on radio and service context (Doppler, delay spread, SINR/INI (Inter Numerology Interference), QoS latency), typically via rules or ML policies that activate suitable BWPs and keep INI under control; higher μ shortens symbols for mobility/latency, while lower μ maximizes PRBs/Hz for eMBB efficiency.
What the diagram shows
a. Inputs: radio context (Doppler, RMS delay spread, SINR/INI maps, FR band) and service context (URLLC/eMBB, latency/BLER targets). The engine also checks UE/gNB capability and policy constraints (supported μ, guard budgets).
b. Decision engine: rules + ML map features to {μ choice, BWP size, guard size, minislot/slot config} with hysteresis/dwell to avoid thrashing; output goes to scheduler that partitions bandwidth into numerology domains or collapses to a single μ when possible.
c. Outcomes: robustness vs Doppler, latency fit for URLLC, and controlled inter‑numerology interference through guards and co-siting.
Careful control-channel design and overhead trimming (e.g., compact DCI, CSI reporting compression) can raise data RE fraction and realized spectral efficiency per carrier configuration.
References
1. ETSI TS 128 552 V16.9.0 5G; Management and orchestration; 5G performance measurements
3. Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems https://arxiv.org/pdf/1708.00994.pdf
4. Reinforcement Learning for Link Adaptation in 5G-NR Networks EVAGORAS MAKRIDIS https://www.evagoras.org/publication/msc/example.pdf
8. Cell-Free Massive MIMO Networks: Practical Aspects and Transmission Techniques for Radio Resource Optimization MAHMOUD ZAHER https://kth.diva-portal.org/smash/get/diva2:1952410/FULLTEXT01.pdf
9. Evaluating the Impact of Numerology and Retransmission on 5G NR V2X Communication at A System-Level Simulation https://arxiv.org/pdf/2307.14158
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