Today were are off on sideline to the war in Ukraine, but not without our investigative hat on. We will be looking at how they are using drones in sky over Ukraine, but also land and sea based systems too. Our gaze will also turn to Russia but only briefly before drawing it in with the all human cost of the drone war in Ukraine.

Then we will look at an academic article. The article considers a new line of sight technology for using drones as interference for ground communications. A new anti-jamming framework is proposed, which exploit hostile jamming signals. But before we get to that, we as always return to new for the web.

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The war in Ukraine will be remembered not for its static front lines, but for the technological revolution it unleashed. This transformation has been spearheaded by the widespread adoption of unmanned systems, which have fundamentally altered battlefield tactics, strategic calculations, and the very nature of modern combat. Inexpensive, innovative, and rapidly adaptable drones have evolved from simple reconnaissance tools into critical instruments of air, land, and sea power, reshaping the conflict at every level.

According to Professor Cross of Northeastern University, drones have been a decisive "game-changer" in the war. These systems, ranging from small, commercially derived quadcopters to sophisticated long-range maritime vessels, have demonstrated an unprecedented ability to project force asymmetrically. They have impacted everything from tactical engagements in frontline trenches to global economic strategy through attacks on energy infrastructure. This article will explore the arc of this drone revolution, from its origins in battlefield necessity to its current role in a high-stakes technological arms race and its profound implications for the future of conflict itself.

To understand the strategic landscape of the war in Ukraine, one must first appreciate the origins of it’s drone prowess. Ukraine's rapid innovation was not a product of long-term military planning but a direct response to confronting a numerically superior adversary while facing significant shortfalls in conventional weaponry, particularly artillery. This reliance on First-Person View (FPV) drones was not a choice but a strategic imperative.

This bottom-up innovation, which Professor Cross describes as "unprecedented," saw Ukrainian engineers and soldiers transform commercially available parts from China into highly effective military assets. This approach brought a new face to modern warfare, frontline FPV drone teams. The experience of 'Darwin,' a 21-year-old ace pilot with the Achilles Battalion, 92nd Brigade, illustrates this reality. From secret locations within range of Russian artillery, he and his team launch dozens of missions in a single day, maintaining intense concentration amid the non-stop shelling to hunt their targets.

The tactical and economic logic behind this strategy is compelling. FPV drones became what many considered Ukraine's "best hope" for combating Russia's vast fleets of tanks and armoured personnel carriers. The cost-effectiveness of this approach is staggering; Ukraine's chief of military production shared the figure of approximately $1,700 per kill. This low cost, contrasted with the high value of the military hardware being destroyed, underscores the asymmetrical power of this new form of warfare.

Ukraine's drone revolution gained strategic significance by refusing to remain a single-domain phenomenon, instead evolving into a multi-pronged assault on Russia's conventional military assumptions in the air, on land and at sea. This expansion created a truly tri-domain unmanned conflict, forcing a complete rethinking of battlefield survivability and operations for both sides.

In the air and on land, the proliferation of drones has expanded the kill zone. With drones now responsible for an estimated 60 to 70 per cent of casualties, traditional troop movements, logistics, and resupply missions have become nearly impossible in some areas. This new reality spurred the emergence of unmanned ground robots. In a dramatic incident, soldiers from the 3rd Assault Brigade witnessed two Russian soldiers surrender to an aerial drone after a ground-based kamikaze drone attacked their dugout. The Brigade believes this was the first successful assault carried out exclusively by robots. These ground drones have also proven invaluable for novel operations, as demonstrated when a unit used one to steal a Russian PKM machine gun directly from an enemy position.

The most significant strategic success has come in the maritime domain. Despite possessing a negligible conventional navy, Ukraine used unmanned sea drones to challenge and effectively neutralise Russia's once-dominant Black Sea fleet. According to the commander of Group 13, the specialised unit operating these drones, their attacks have forced Russian naval vessels to hide and limit their movements. This pressure allowed Ukraine to reopen vital grain and metal export routes, a crucial achievement for its wartime economy. This three-pronged unmanned strategy showcases a paradigm shift in asymmetric warfare, setting the stage for even bolder strategic applications of the technology.

The evolution of Ukraine's drone program reached an inflexion point when it shifted from a purely tactical asset to an instrument of national economic warfare. Frustrated with the limitations of Western sanctions, Ukraine began a campaign of "kinetic sanctions," using long-range and naval drones to attack Russia's energy economy, thereby threatening the primary revenue stream funding the Kremlin's war machine.

Oleksandr Kubrakov, then serving as the minister of infrastructure, recalled denying permission to sink a massive oil tanker in August 2023 because it flew a non-Russian flag. However, as the war dragged on, Ukraine set aside earlier rules of engagement. After its successful naval drone campaign against warships forced the retreat of the Russian fleet and secured a Black Sea export corridor, a delicate balance was established. Ukraine broke that balance by escalating its attacks to target the "shadow fleet" of oil tankers transporting Russian crude in international waters.

Ukraine's initial innovative edge was not permanent. It triggered a dynamic and unending cycle of adaptation and counter-adaptation that is defining the next phase of the conflict. The battlefield has become a high-speed laboratory where new technologies are deployed, countered, and superseded in a matter of months, forcing both sides into a relentless technological arms race.

Russia, after initially being caught off guard, moved quickly to respond. It adapted to Ukrainian tactics, copied successful designs, and significantly ramped up its own drone production. Critically, Russia gained a distinct operational edge by deploying fibre-optic drones. Tethered to an operator by miles of ultra-thin cable instead of jammable radio signals, these systems are a nightmare for infantry. According to Commander Yurih' Achilles' Fedorenko of the 429th Separate Regiment of Unmanned Systems, Russia's advantage in this domain is fuelled by external support, claiming that China is supplying key components at a nine-to-one ratio compared to Ukraine.

As this competition intensifies, the next technological frontier is artificial intelligence. Developers are integrating AI for semi-autonomous target guidance, allowing a drone to lock onto a target and complete its mission even after losing connection to the operator. Experts are also developing systems for "drone group tasking," the operational deployment of swarms, and training AI models on thousands of hours of combat footage to automatically detect enemy targets. The pace of this innovation is relentless. As one Ukrainian developer noted, the expectation is that within a year, 90 per cent of successful drone operations will likely be influenced by AI. This rapid acceleration, however, raises a final, crucial question: amidst this robotic arms race, what is the ultimate role of human beings?

While drones have undeniably revolutionised the war in Ukraine, creating a more lethal, expansive, and transparent battlefield, they have not created a fully automated conflict. The narrative of a purely robotic war, fought by machines from the safety of a command bunker, remains a misconception. The conflict has demonstrated that even the most advanced unmanned systems are tools, and their effectiveness is inextricably linked to the people who build, deploy, and command them.

Cautionary perspectives from the front lines emphasise this crucial distinction. Commander 'Achilles' Fedorenko argues that drones cannot fully replace essential military capabilities. In poor weather conditions, such as heavy rain or snow, when drones are often unable to fly, it is artillery that must still fulfil the mission. Similarly, unmanned systems cannot hold ground; that task still falls to infantry. His assessment offers a vital dose of realism in an era captivated by technological promise: "people continue to be the main capital of war."

The war in Ukraine has become the world's foremost laboratory for drone warfare, offering a clear glimpse into the future of conflict. This future lies not in the replacement of humans by machines, but in the seamless integration of human ingenuity with increasingly autonomous systems. The ultimate outcome of this war, and those that will follow, will depend not just on the sophistication of the technology but on the strategic vision, resourcefulness, and endurance of the people who wield it.

If you want to know more, click on these links: link 1; link 2; link 3; link 4; link 5; link 6.

This paper addresses the critical challenge of countering intelligent Unmanned Aerial Vehicle (UAV) jamming within energy-constrained ambient backscatter communication (AmBC) systems. A novel anti-jamming framework based on Deep Reinforcement Learning (DRL) is proposed, enabling the transmitter to strategically exploit hostile jamming signals to enhance communication resilience.

Wireless communication systems are fundamentally vulnerable to malicious interference, specifically jamming attacks, which intentionally transmit disruptive signals to severely degrade the Signal-to-Interference-plus-Noise Ratio (SINR) at legitimate receivers. These attacks pose a significant threat to network reliability, especially considering the recent proliferation of Unmanned Aerial Vehicles (UAVs). UAVs introduce a new dimension to this threat, serving as agile, mobile, and intelligent jammers capable of maximising disruption by leveraging strong Line-of-Sight (LoS) links, making them more severe adversaries than static ground jammers. Furthermore, traditional anti-jamming methods, such as power control or frequency hopping, are generally insufficient because they struggle to adapt to the learning and shifting strategies of intelligent adversaries. This lack of adaptability is particularly acute in low-power systems like those addressed in this work.

The specific context of this work is ambient backscatter communication (AmBC), a paradigm that allows resource-constrained devices to communicate with ultra-low power consumption by reflecting existing ambient RF signals towards a receiver. AmBC is often implemented alongside principles of Simultaneous Wireless Information and Power Transfer (SWIPT), allowing devices to harvest energy from these same RF signals. Previous works explored reinforcement learning (RL) or Deep Q-Network (DQN) approaches for anti-jamming, but they often focused on channel switching or power adaptation in active systems, or did not thoroughly investigate the dynamic exploitation of the jamming signal itself. Therefore, the core challenge addressed here is the strategic interplay among active transmission, energy harvesting from the jamming signal, or passive backscattering using the jammer's emission, all while facing a sophisticated UAV jammer.

The primary use case is ensuring resilient and high-throughput communication for energy-constrained devices operating in environments subjected to dynamic, intelligent UAV jamming. The proposed solution provides a multifaceted response capability for the transmitter, allowing it to dynamically choose an optimal operational mode from a rich action space defined by its versatility, which includes Active Transmission (AT), Energy Harvesting (EH), Ambient Backscattering (AmBC), and Rate Adaptation (RA). This strategy is crucial because the transmitter operates under partial observability, detecting only the presence or absence of jamming (binary status), but not the jammer's specific power level. By framing this strategic decision-making process as a Markov Decision Process (MDP), the system model accounts for critical factors like the jamming status, the transmitter's data buffer occupancy, and its current stored energy level.

The implementation of the Deep Q-Network (DQN) solution serves to calculate the optimal policy for maximising the long-term average system throughput in this uncertain, non-stationary environment. The DQN agent is used to implicitly learn the intelligent UAV jammer's behaviour and the complex environmental dynamics without requiring explicit knowledge, which is critical given the jammer's dynamic positioning and power adjustments. Simulation results validate the use case by showing that the DQN-based method achieves significant performance gains—higher throughput and packet delivery ratio, and lower packet loss—compared to a fixed, non-adaptive greedy strategy. Essentially, this framework's application allows low-power devices to utilise an antagonistic signal as an opportunistic resource for communication or energy replenishment, enhancing system resilience against sophisticated aerial threats.

"The transmitter can choose to: (i) actively transmit packets using stored energy when the channel is clear, (ii) harvest energy from the UAV's jamming signal, or (iii) backscatter its data using the ongoing jamming signal."

The main conclusion is that the proposed Deep Q-Network approach successfully addresses intelligent UAV jamming in ambient backscatter communication systems. It enables the learning agent to effectively transform an adversarial jamming signal into a useful, opportunistic resource, thereby enhancing system throughput and resilience. The simulation results confirmed DQN's superior performance over a static greedy baseline across key metrics. Future research can extend this anti-jamming framework by exploring scenarios involving multi-agent Deep Reinforcement Learning. Additionally, next steps should include investigating more complex jamming strategies or incorporating physical layer security metrics into the system design.

You can download the article here.