Directional SDR Jamming Attacks

• Directional SDR jamming is a technique that employs beamforming, phase control, and spatial steering via SDRs, RIS, and UAVs to target interruptions while protecting desired nodes.
• It leverages precise control of antenna orientation, trajectory, and programmable phase shifts to create focused jamming hotspots with specific power metrics and nulling effects.
• Experimental validations demonstrate its effective use in both lab and field environments, highlighting critical trade-offs between mobility, exposure time, and jamming intensity.

Directional Software-Defined Radio (SDR) jamming attacks leverage spatial control over interference to selectively disrupt wireless communication targets while sparing others. Recent advances utilize coordinated UAV-based beamforming, software-driven control of antenna illumination, and reconfigurable intelligent surfaces (RIS) to perform precise electronic attacks in complex radio-frequency (RF) environments. Directional SDR jamming now encompasses a continuum from mobile null-steering arrays to stationary RIS-enhanced attacks, with empirical validations in both laboratory and field-deployed private 5G networks (Fotiadis et al., 24 Aug 2025, Mackensen et al., 21 Feb 2024, Mykytyn et al., 3 Dec 2025).

1. System Models and Architectures

Directional SDR jamming–in all modern incarnations–relies on the precise shaping of the jamming beampattern via spatial, phase, and mobility controls. Three principal architectures, extracted from the literature, are representative:

Approach	Control Mechanism	Spatial Selectivity
UAV beamforming/nulling	Mobile array phase, trajectory	Degrees via trajectory and orientation (Fotiadis et al., 24 Aug 2025)
RIS-enhanced SDR jamming	Programmable reflecting phases	Sub-wavelength, focal spot (Mackensen et al., 21 Feb 2024)
Fixed SDR + directional panel	Antenna alignment, barrage	25° HPBW, intensity hotspot (Mykytyn et al., 3 Dec 2025)

UAV-based beamforming employs two omnidirectional transmit antennas on a UAV, separated by baseline $D$, with real-time adjustment of array orientation $\theta_g$, position ${\bf p}_g(t)$, and transmit phases $\phi_1(t), \phi_2(t)$. The system operates in a 2D Euclidean geometry with fixed client and eavesdropper positions ${\bf p}_c, {\bf p}_e$ (Fotiadis et al., 24 Aug 2025).

RIS-enhanced jamming is realized via passive reconfigurable intelligent surfaces co-located with an SDR source. Each element on the RIS imparts a binary phase shift $c_l$ to impinging waves, forming effective programmable phased arrays that can create highly localized interference “focal spots,” even when targets are separated by millimeter-scale distances (Mackensen et al., 21 Feb 2024).

Stationary directional-jammer architectures for field deployment use high-gain panel arrays (e.g., 15 dBi directive, 25° HPBW), managed through SDR hardware and software, to sustain a region of high jamming intensity at a distance, and can sweep frequency/power to adapt to changing targets in mobile scenarios (Mykytyn et al., 3 Dec 2025).

2. Signal Processing and Beamforming/Forming Nulls

Directional SDR jamming effectiveness is achieved by maximizing interference at designated targets while enforcing deep nulls (suppression) toward protected nodes.

UAV Null-Steering and Beamforming

• Beampattern: For two-element arrays, the beamforming gain at position $p$ is

$$B(p) = |e^{j[k d_1(p) + \phi_1]} + e^{j[k d_2(p) + \phi_2]}|^2$$

with $d_i(p) = \|p - p_i\|$, $k = 2\pi/\lambda$.

• Nulling: Enforce $B({\bf p}_c) = 0$ by choosing

$$\phi_2(t) = \phi_1(t) + \pi + k[d_1({\bf p}_c) - d_2({\bf p}_c)]$$

ensuring exact zero jamming at the client position at all times.

• Orientation optimization: For target position ${\bf p}_e$, maximize $B({\bf p}_e)$ by analytical or numerical selection of $\theta_g(t)$ pointwise, using closed-form approximations (e.g., maximizing $B^*$ in far-field via the angular separation parameter $\mu$) (Fotiadis et al., 24 Aug 2025).

RIS-Driven Directionality

• RIS transfer function: For device $D_i$,

$$H^{\rm RIS}_{D_i}(c) = \sum_{l=1}^L h_{\rm SDR\rightarrow RIS, l} \cdot c_l \cdot h_{\rm RIS\rightarrow D_i, l}$$

• Beam control: To jam in direction $\theta_i$, set phases $$c_l \approx e^{-jk(x_l \sin\theta_i + y_l \cos\theta_i)}$$.
• Nulling: For neighbor device in direction $\theta_j$, solve

$$\sum_{l=1}^L c_l \, e^{jk(x_l \sin\theta_j + y_l \cos\theta_j)} \approx 0$$

Optimization utilizes genetic/greedy feedback-driven loops, guided by RSSI/CSI measurements, to empirically maximize the jamming at targets while minimizing leakage to others, achieving spatial selectivity below the wavelength threshold (Mackensen et al., 21 Feb 2024).

3. Experimental Results and Network Impact

Directional SDR jamming has been validated in both controlled laboratory and operational field environments.

UAV/SDR Nuller-Jammer Field Studies

• Achieves exact null ($P({\bf p}_c)$ approaches $-\infty$ dBm) toward the protected client, while jamming eavesdropper positions to denial-of-service (DoS) levels ($P^* > -90$ dBm) as soon as angular separation allows for nondegenerate steering (Fotiadis et al., 24 Aug 2025).
• Jamming impact is dynamically sculpted by UAV trajectory: as UAV maneuvers to increase $|\theta_c - \theta_e|$, jamming at the eavesdropper rises from no effect ($B^*=0$) to maximal ($B^*=4$, $+6$ dB) while client remains unaffected.
• Realized in real time using SDR platforms (e.g., GNU Radio, I/Q-modem driven phase control, autopilot-driven array position/orientation updates).

RIS-Enabled Micro-Targeted Jamming

• In a 9 × 7.5 m indoor Wi-Fi environment with a 768-element binary RIS:
  • Single-target jamming is achieved with –21 dBm SDR output; non-target devices sustain full operation unless jamming power is increased by >17 dB.
  • Sub-wavelength selectivity demonstrated: two devices separated by 5 mm can be independently jammed.
  • Jamming “focal spot” of ±6 cm; outside which RSSI drops by >5 dB (avg 13 dB) (Mackensen et al., 21 Feb 2024).

Mobility and Exposure Effects in 5G UAV Scenarios

• Directional jamming using high-gain SDRs creates a hot-zone with radius $\sim$300 m.
• At low UAV speeds ($3$–$6$ m/s), link collapse is prevalent due to prolonged exposure (BLER $> 50\%$, throughput outages, Radio Link Failure).
• For $v=12$ m/s, exposure time per traversal through jamming lobe drops below $5$ s, avoiding sustained outages (BLER $< 10\%$, no RLF).
• Parameter thresholds for robust link: ensure $t_{\rm exp} = 2 D_{\rm lobe}/v < 5$ s and $\mathrm{SINR} > 5$ dB (Mykytyn et al., 3 Dec 2025).

4. Mathematical Models and Optimization Criteria

The theoretical backbone of directional SDR jamming includes combined beamforming design, optimal control, and environment-adaptive optimization.

• Null-steering control:
  • For $N$-element array on UAV, maximize

  $$\text{maximize}_{\mathbf{w}} \frac{\mathbf{w}^H R_s \mathbf{w}}{\mathbf{w}^H R_j \mathbf{w}}$$

  where $R_s$ and $R_j$ are covariance matrices for desired and jamming signals (beamforming for communication, null-steering for jammer suppression) (Mykytyn et al., 3 Dec 2025).

• UAV optimal control:

  • Cost functional $J$ is minimized over trajectory and orientation, balancing energy, control effort, and jamming “reward,” with constraints for exact nulling and trajectory actuation limits (Fotiadis et al., 24 Aug 2025).
  • Pontryagin’s principle yields bang-bang/linear-feedback control law for acceleration, respecting actuator limits.
• RIS element optimization:
  • Discrete-phase optimizer iterates to maximize jamming at targets subject to leakage constraints at non-targets, using only RSSI/CSI feedback and measurement-driven greedy-genetic search (up to $10^4$ iterations per optimization) (Mackensen et al., 21 Feb 2024).

5. Countermeasures and Mitigation Strategies

No cryptographic mechanism can a priori prevent spatially focused SDR/RIS jamming; defense strategies all operate at the physical, MAC, or network layers.

• Antenna diversity and digital null-steering: Equipping UAVs or UEs with $N$-element arrays enables beamforming to maximize $R_s$ while nulling out $R_j$, yielding $> 20$ dB jammer suppression (Mykytyn et al., 3 Dec 2025).
• Adaptive power/frequency hopping: Adjusting per-subband power via inverse water-filling ($P_t(f) \propto 1/(|H(f)|^2+\lambda)$), or slot-level hopping, disrupts static or frequency-swept barrage jammers.
• Network hardening: Fast handover, dual-connectivity to multiple base stations, and lowering handover thresholds (e.g., event A3/A5) reduce exposure time to directional jamming lobes.
• Defensive channel obfuscation: Randomize transmitted power, MAC-layer identifiers, or beam patterns to frustrate measurement-driven RIS optimization—albeit at the cost of throughput or with possible circumvention via RF fingerprinting (Mackensen et al., 21 Feb 2024).
• Rapid detection: High BLER variance and CQI drop monitoring can automate evasive action or prompt handover (Mykytyn et al., 3 Dec 2025).

6. Practical Implementations and Design Guidelines

Real-world realization employs commodity SDRs (e.g., USRP B210), high-gain panels, and, in advanced setups, FPGA or fast digital control for RIS/array phase adjustments.

• UAV SDR nullers: Digital I/Q phase control in GNU Radio, pan/tilt array orientation via autopilot commands, and real-time feedback from SDR host integrating position updates.
• RIS jammers: SDR generates arbitrary waveform fed to RIS, with measurement-driven closed-loop feedback to optimize binary phase settings.
• Operational guidelines: Maintain UAVs at cruise speeds $> 10$ m/s, deploy at least 4-element arrays for digital beamforming, and configure handover protocols to minimize time in jamming lobes. Adherence to key formulas and parameter thresholds (e.g., $t_{\rm exp}$, $\mathrm{SINR}$) is necessary for resilience planning (Mykytyn et al., 3 Dec 2025).

7. Impact and Research Directions

Directional SDR jamming raises the efficacy and threat profile of wireless jamming attacks, especially as RIS technology matures and integrable beamforming capabilities become widely available on SDR and UAV platforms. These methods outpace legacy countermeasures and require the development of agile, cross-layer defense mechanisms. Current literature highlights both the spatial precision attainable (down to millimeter separation) and the operational limitations (such as exposure-time dependency on UAV speed and trajectory), motivating further research on real-time optimization, channel obfuscation, and robust network-layer hardening (Fotiadis et al., 24 Aug 2025, Mackensen et al., 21 Feb 2024, Mykytyn et al., 3 Dec 2025).