As 5G and 5G-Advanced push uplink efficiency and modulation performance, engineers are revisiting where power amplifier (PA) linearization should happen and how it should be validated. Hardware-in-the-loop testing of AI-based digital post-distortion (DPoD) is now linking today’s 5G measurements with possible 6G receiver architectures.
PA linearity remains one of the hardest tradeoffs when designing wireless communication systems. A user device can operate its PA in the linear region with power backoff to preserve signal quality, but that reduces efficiency and drains battery life. Or it can drive the PA closer to saturation, improving power efficiency while introducing in-band distortion and out-of-band spectral regrowth. In 5G, and increasingly in 5G-Advanced, that compromise is becoming more visible as uplink performance demands rise.
This is one reason why DPoD is attracting attention. Unlike conventional digital pre-distortion, which linearizes the PA at the transmitter, DPoD shifts part of the compensation burden to the receiver. In the cellular uplink, that means the base station attempts to recover a signal that may have been transmitted by a more efficient, more nonlinear user device. The idea is especially relevant for battery-powered equipment at the cell edge, where uplink power efficiency matters most.
Although DPoD is often discussed in the context of 6G, its practical evaluation starts with 5G testing. The key questions are measurable today: How much uplink distortion can a receiver tolerate? Can AI-based receivers recover signals beyond what conventional algorithms can handle? And what test setup is needed to compare the two under realistic conditions?
Why the uplink remains difficult
The peak-to-average power ratio of OFDM signals in the 5G uplink leads to nonlinear distortions when operating the PA of the user equipment (UE) close to saturation. As modulation orders increase, the margin for impairment shrinks further. Higher-order constellations improve spectral efficiency, but they are also more vulnerable to error-vector-magnitude degradation caused by PA compression and phase distortion.
Traditionally, this problem has been managed at the UE with transmitter linearization, calibration, and careful PA operation. But that approach carries costs in complexity, power consumption, and thermal overhead. For smartphones, wearables, and IoT devices, those penalties are not trivial.
That is why receiver-side compensation is being revisited, particularly as 5G-Advanced sharpens focus on uplink performance and 6G research considers more aggressive spectral-efficiency targets.
DPoD does not remove the need for spectral compliance, and it does not make PA nonlinearity harmless. What it does offer is a different system partitioning: Some of the burden of recovering a distorted uplink is shifted from the device to the network receiver.
Why AI-based DPoD matters
Classical post-distortion methods rely on predefined models of nonlinear behavior. Those methods can work well in controlled cases, but real uplink signals are shaped by multiple effects at once: PA nonlinearity, multipath fading, noise, synchronization error, and implementation nonidealities. That makes a purely model-based approach increasingly difficult, especially when thinking beyond current 5G deployments toward 5G-Advanced and 6G.
AI-based receivers are attractive because they can learn from representative data rather than relying on a fixed analytical model. One example is Nokia Bell Labs’ HybridDeepRx, a neural-network-based receiver designed for OFDM waveforms affected by channel impairments and transmitter nonlinearity (Figure 2). In the setup discussed here, HybridDeepRx effectively replaces part of the conventional base-station receiver chain.
Its role is not simply to “denoise” the signal. The model jointly addresses channel-related effects and nonlinear distortion, then performs demapping to generate soft information for decoding. A key feature of the architecture is that it alternates between frequency-domain and time-domain processing.
Frequency-domain stages help address channel effects in a way familiar to conventional OFDM receivers, while time-domain stages are well-suited to mitigating PA-induced distortion. This hybrid structure makes it a useful candidate for testing whether AI-based DPoD can outperform conventional uplink receiver processing when the transmitter is deliberately operated in a more nonlinear region.
Hardware-in-the-loop testbed
To evaluate that question credibly, Nokia Bell Labs and Rohde & Schwarz (R&S) used a hardware-in-the-loop testbed built around standard-compliant 5G signal generation and wideband signal analysis. The objective was to compare conventional receiver processing with AI-based reception under controlled but realistic uplink impairment conditions.
Figure 3 shows the overall setup. An R&S SMW200A vector signal generator creates the 5G uplink waveform and applies a PA model, followed by wireless channel emulation. This is important because it allows a controlled introduction of nonlinear PA distortion without changing physical hardware from test to test. Researchers can vary amplifier operating point and distortion level repeatably, which is essential for comparing conventional and AI-based receiver behavior.
On the receive side, an FSWX signal and spectrum analyzer captures the impaired uplink waveform. The FSWX provides the bandwidth and dynamic range needed for this kind of work. The captured signal is then processed in the R&S VSE vector signal explorer software, which serves as the central analysis environment.
VSE plays several roles in the setup: It performs the standard demodulation steps needed for signal analysis; supports KPI extraction such as BLER, BER, throughput and ACLR; and, crucially, can load user-defined AI models in ONNX format. In this case, Nokia Bell Labs’ HybridDeepRx receiver is imported into the measurement flow and executed using GPU-accelerated inference. That creates a direct bridge between AI model development and RF test instrumentation: The same captured waveform can be processed through a conventional algorithm chain or the neural-network-based receiver.
This is where HybridDeepRx becomes central to the measurement concept. Rather than evaluating an abstract AI model offline, the setup places the model directly inside a realistic signal-analysis flow. That allows the comparison of receiver approaches using the same waveform, same impairment conditions, and same measured KPIs such as block error rate and throughput. For engineers, this is much more convincing than a purely simulated benchmark.
The value of the testbed is not limited to proving that an AI receiver can run in the loop. It also helps clarify how much distortion can be tolerated before conventional processing breaks down and whether an AI-based receiver extends that operating region.
Because the R&S SMW200A can impose both wireless-channel effects and controlled PA nonlinearity, the setup can explore a range of realistic uplink cases, from mildly impaired links to strongly compressed transmission. This makes it possible to study a key architectural question for future systems: If the receiver becomes more intelligent, can the UE be allowed to transmit less linearly and therefore more efficiently?
That question matters because receiver-side compensation may improve bit recovery, but it does not remove the physical consequences of PA compression. The value of a hardware-in-the-loop setup is that it keeps the evaluation grounded in measurable signal behavior and system-level KPIs rather than algorithm claims alone.
From 5G measurement to 6G architecture
This makes AI-based DPoD a good example of how 5G testing is feeding future wireless architecture. The waveforms, KPIs, and measurement discipline are rooted in today’s 5G and 5G-Advanced work. But the system question being explored points toward 6G: whether some of the uplink linearization burden can move from power-limited devices to compute-rich infrastructure.
That possibility is attractive because the uplink is where device constraints are least forgiving. If future base stations can recover more heavily distorted signals, UEs may be able to operate with simpler or more efficient transmitters in difficult link conditions. For cell-edge users, that could translate into better power efficiency without an equivalent penalty in coverage or throughput.
The concept is still emerging, and many challenges remain, including generalization across devices, channels, and deployment conditions. But the combination of a realistic 5G waveform source, controlled PA and channel emulation, wideband signal capture, and in-tool AI inference provides a practical way to study the idea now.
AI-based post-distortion is not just a theoretical 6G topic; it is already a 5G test problem. And as this work shows, progress depends on hardware-backed comparison between established receiver methods and AI-based alternatives, using tools that let both live in the same measurement flow.
About the author
Andreas Oeldemann is program manager of AI for wireless at Rohde & Schwarz, headquartered in Munich. As part of the corporate R&D team, his work focuses on how novel T&M solutions will enable the validation and deployment of physical- and MAC-layer AI/ML methods of 5G-Advanced and 6G communication systems. In this role, he investigates future customer requirements for testing AI/ML implementations in the air interface, develops early proof-of-concept T&M solutions, and drives collaborations with industry partners and academic institutions to advance 6G R&D. He holds an M.Sc. in electrical engineering from the Technical University of Munich.
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