Qualcomm X105: first Release 19 modem and what it means for field diagnostic tools

On March 4, 2026, at Mobile World Congress in Barcelona, Qualcomm announced the Snapdragon X105 modem, the first commercial chipset targeting 3GPP Release 19 features. This is not an incremental update. Release 19 introduces AI/ML as a native component of the air interface, fundamentally changing what happens between the UE and the network. For field diagnostic tools that rely on Qualcomm’s DIAG interface to extract layer 3 messages and RF measurements, the X105 represents both an opportunity and a challenge.

What the X105 brings

Architecture overview

The X105 is Qualcomm’s next-generation 5G-Advanced modem, succeeding the X80 (Release 18) and X75 (Release 17). Key specifications:

Parameter	X105 (Release 19)	X80 (Release 18)	X75 (Release 17)
3GPP release target	Release 19	Release 18	Release 17
Max DL throughput	12 Gbps	10 Gbps	10 Gbps
Max UL throughput	5 Gbps	3.5 Gbps	3.5 Gbps
Carrier aggregation	6CC (sub-6 GHz)	5CC	4CC
AI/ML engine	Integrated (on-modem)	Accelerator (co-processor)	None
FR2 support	n257, n258, n259, n261, n262	n257, n258, n261	n257, n258, n261
Process node	3 nm (TSMC N3E)	4 nm	4 nm
Power efficiency	+30% vs X80	Baseline	Baseline
Positioning	cm-level (carrier phase)	Sub-meter	Sub-meter

The headline feature is the integrated AI/ML engine on the modem die itself. Previous generations used a separate accelerator for AI tasks. The X105 puts the inference engine directly in the modem’s signal processing pipeline, enabling real-time AI-based decisions at the physical layer.

AI-based CSI compression: +40% DL throughput

Channel State Information (CSI) feedback is the mechanism by which the UE tells the base station about the current channel conditions. The gNB uses this information to select the optimal precoding matrix, modulation scheme, and resource allocation. In current networks (Release 17/18), CSI feedback uses codebook-based reporting with a fixed set of predefined channel representations.

Release 19 introduces AI-based CSI compression, where the UE uses a neural network to compress the channel state into a compact representation and the gNB uses a paired neural network to decompress it. The result:

• CSI feedback overhead reduced by 70-80% compared to codebook-based reporting
• More accurate channel representation because the AI model captures channel characteristics that codebooks cannot
• Net effect: approximately +40% downlink throughput under realistic conditions, because the gNB can make better precoding decisions with more accurate channel knowledge

For field diagnostic tools, this creates a new measurement domain. The traditional CSI KPIs (CQI, PMI, RI) are supplemented by AI-specific metrics:

Traditional KPI	AI-enhanced equivalent	What it measures
CQI (Channel Quality Indicator)	AI CSI confidence score	How confident the AI model is in its channel estimate
PMI (Precoding Matrix Indicator)	AI precoding vector	The AI-selected precoding, not from a fixed codebook
RI (Rank Indicator)	AI rank estimation	Spatial multiplexing capability estimated by ML
N/A	CSI compression ratio	Bits used for AI CSI vs. codebook CSI
N/A	AI model version	Which trained model is active on the UE

ML beam management

Release 19 formalizes machine learning-based beam management, replacing the exhaustive beam sweep procedure with a predictive beam selection model. Instead of the UE measuring all SSB beams and reporting the best, the ML model predicts the optimal beam based on:

• Recent beam measurement history
• UE mobility pattern (speed, direction)
• Timing and frequency offset trends
• Spatial channel characteristics from the AI CSI engine

The practical benefit is reduced beam management overhead. In current FR2 deployments, beam sweep can consume 20-30% of available resources. ML beam management can reduce this to 5-10%, freeing capacity for user data.

For field testing, ML beam management introduces non-deterministic behavior. The same physical location, same time of day, same UE orientation may produce different beam selections on consecutive measurements because the ML model’s state depends on the measurement history leading up to that point. This challenges the reproducibility assumption that underpins traditional drive test methodology.

High-precision positioning

The X105 supports centimeter-level positioning using carrier phase measurements. This is a Release 19 feature that combines:

• NR PRS (Positioning Reference Signal) measurements
• Carrier phase ranging (similar to RTK GNSS but using cellular signals)
• AI-based position estimation that fuses multiple signal sources

For field diagnostic tools, cm-level positioning means that measurement location accuracy is no longer limited by GPS (typically 3-5 meters). This enables indoor positioning for in-building validation without external GNSS reference, precise geo-referencing of interference sources, and repeatable measurement campaigns where engineers can verify they are testing at the exact same physical point.

Implications for DIAG-based diagnostic tools

The DIAG interface evolution

Qualcomm’s DIAG protocol is the primary interface through which diagnostic tools extract modem-level data. Every Qualcomm modem generation exposes new DIAG log codes corresponding to new features. The X105 will introduce new log codes for:

AI CSI logs

• AI CSI report content (compressed representation)
• AI model identifier and version
• CSI compression ratio per report
• AI confidence metrics
• Fallback events (when the AI model reverts to codebook CSI)

ML beam management logs

• Predicted beam index vs. measured best beam
• Prediction accuracy metrics
• ML model state updates
• Beam prediction latency

Positioning logs

• Carrier phase measurements
• AI-fused position estimates
• Positioning accuracy confidence intervals

Diagnostic tools must parse these new log codes to provide meaningful field measurements. Tools that only support Release 17/18 log codes will miss the AI-layer KPIs entirely, reducing their diagnostic value on X105-equipped devices.

New KPIs for field validation

The X105 requires field engineers to add new KPIs to their validation frameworks:

AI CSI performance KPIs

KPI	Definition	Target range
AI CSI gain	Throughput improvement vs. codebook CSI	>25%
AI CSI fallback rate	% of reports reverting to codebook	<5%
AI model convergence time	Time for AI CSI to outperform codebook after cold start	<500 ms
Compression efficiency	Bits saved vs. codebook reporting	>70%
AI CSI BLER correlation	Does AI CSI reduce BLER vs. codebook?	BLER reduction >15%

ML beam management KPIs

KPI	Definition	Target range
Beam prediction accuracy	% of correct beam predictions	>85%
Beam switch latency (ML)	Time to switch to ML-predicted beam	<2 ms
Resource savings	Reduction in beam sweep overhead	>50%
ML beam failure rate	Incorrect predictions causing RLF	<0.1%
Mobility prediction accuracy	Correct beam prediction during movement	>75% at pedestrian speed

High-precision positioning KPIs

KPI	Definition	Target range
Horizontal accuracy (90th percentile)	Positioning error radius	<10 cm (ideal), <50 cm (typical)
Positioning latency	Time to first fix	<200 ms
Indoor availability	% of indoor locations with cm-level fix	>60% (in NR PRS coverage)
Positioning continuity	Fix maintenance during mobility	>95%

The diagnostic tool adaptation challenge

Field diagnostic tools face a specific challenge with the X105: AI-layer behavior is not fully observable through DIAG. While Qualcomm will expose summary metrics (compression ratio, confidence scores), the internal state of the neural network is not accessible. This means:

• You can measure the output of AI CSI (the compressed report and resulting throughput) but not the internal model weights or activations
• You can observe ML beam predictions but not the reasoning behind a specific prediction
• You can detect AI fallback events but diagnosing why the model fell back requires correlation with RF conditions, mobility, and channel state

This shifts field testing from purely deterministic measurement toward statistical characterization. Instead of saying “at this location, with this RSRP, the throughput is X,” the statement becomes “at this location, over N measurements, the AI CSI delivered Y% throughput gain with Z% confidence.”

Chipset alternatives

Qualcomm is not the only player. The X105 competes with:

Samsung Shannon 5400

Samsung’s next-generation modem, expected in the Galaxy S27 series (late 2026), targets Release 18+ features. Samsung has been more conservative on AI air interface adoption, focusing on AI for power management and mobility optimization rather than CSI compression. The Shannon DIAG equivalent (Samsung’s diagnostic interface) is less widely documented, making third-party diagnostic tool support more challenging.

MediaTek Dimensity 9500

MediaTek’s flagship modem for 2026-2027 targets Release 18 with selected Release 19 features. MediaTek has historically provided less DIAG-level access than Qualcomm, which limits the depth of field diagnostics possible on MediaTek-powered devices. However, MediaTek’s growing share in mid-range 5G devices (particularly in Africa and Southeast Asia) means field tools cannot ignore this platform.

Feature	Qualcomm X105	Samsung Shannon 5400	MediaTek Dimensity 9500
Release target	19	18+	18 (selective R19)
AI CSI	Full (on-modem)	Partial	Partial
ML beam management	Full	Limited	Limited
DIAG access	Comprehensive	Moderate	Limited
Expected availability	Q4 2026	Q4 2026	Q3 2026
Primary OEMs	Most Android flagships	Samsung Galaxy	Xiaomi, OPPO, vivo

Timeline: from announcement to field deployment

Date	Milestone
March 4, 2026	X105 announced at MWC
Q2 2026	Engineering samples to OEMs
Q3 2026	OEM integration and carrier certification
Q4 2026	First commercial phones with X105
Q1 2027	Volume availability across multiple OEMs
Q2-Q3 2027	Operator network-side Release 19 feature activation
H2 2027	AI CSI and ML beam management in live commercial networks

The critical point: X105-equipped phones will ship in Q4 2026, but the Release 19 network-side features (AI CSI, ML beam management) require gNB software upgrades that most operators will not deploy until 2027. This creates a transition period where the modem supports features that the network does not yet enable.

Field diagnostic tools must handle this gracefully, detecting which Release 19 features are active (network-enabled) versus merely supported (modem-capable) and reporting accordingly.

Preparing for the X105 era

Field engineering teams should take the following steps:

1. Update DIAG log code databases as soon as Qualcomm publishes X105 documentation. New log codes for AI CSI, ML beam management, and carrier phase positioning will be essential.

2. Develop AI-aware KPI frameworks that include the metrics listed above. Traditional RF KPIs remain necessary but are no longer sufficient.

3. Build statistical measurement procedures that account for AI-layer non-determinism. Single-point measurements become less meaningful; statistical distributions over multiple samples become the standard.

4. Test on both Release 18 and Release 19 configurations during the transition period. The same phone may behave differently depending on whether the network enables R19 features.

5. Invest in L3 decoder updates that can parse the new RRC and NAS messages associated with Release 19. AI CSI configuration is negotiated through RRC signaling; the decoder must understand these new information elements.

The X105 is not just a faster modem. It is the beginning of AI-native mobile networks, where the air interface behavior is determined by machine learning models rather than static algorithms. Field diagnostic tools that adapt to this reality will capture insights that legacy tools simply cannot see.

The transition from codebook-based to AI-based channel feedback is the most significant change to the physical layer since the introduction of MIMO. Field tools that cannot parse AI CSI metrics and ML beam management KPIs will be operating blind on Release 19 networks.