CORPA · COgnitive Radio Platform and Algorithms

COgnitive Radio Platform and Algorithms

The information and communications technology industry is currently faced with a global challenge: developing new services with improved quality of service, high quality of experience, and reduced environmental impact. Clearly, there is a pressing need for efficiency in the spectral domain, the energy domain, and the design of the radio. Wireless technology is rapidly working its way into all aspects of computing and communication. This, coupled with an exponentially growing number of military and civilian wireless devices, the explosion of wireless applications and new services, has created a huge demand for spectrum. In the civilian sector, consumer devices from smartphones to IoT devices are competing for bandwidth. In the military, there is a growing reliance on unmanned platforms, from underwater sensors to satellites, and a push for broadband connectivity. It is feared that a spectrum crisis may soon occur where this exploding demand will overwhelm current wireless capacity. This does not only affect smartphones, but all wireless devices. The underlying problem is the lack of new spectrum available to wireless data carriers. Today’s static spectrum allocation approach, which divides the spectrum into exclusively licensed bands, which are allocated over large, geographically defined regions, is not adaptive to the dynamics of supply and demand. At any given time, many allocated bands are unused by licensees while other bands are overwhelmed, thus squandering the spectrum’s enormous capacity and unnecessarily creating conditions of scarcity. Unlicensed bands or shared spectrum provide more flexibility and efficiency in spectrum usage, but need more sophisticated interference avoidance techniques than simple sense-and- avoid techniques in future dynamic and flexible spectrum allocations. To harvest the full capacity out of the RF spectrum, future wireless networks will need to use greater intelligence to avoid interference while optimizing the spectrum by collaborating with other systems that occupy the same spectrum bands. These considerations have motivated research into breakthrough radio technologies that can meet future demands in terms of spectral efficiency, energy efficiency, QoS, QoE, and application performance. This vision of ubiquitous wireless at the edge of the Internet is attractive with many social and individual benefits. However, the anticipated exponential growth of wireless devices and applications depends on the ability to design radio technologies that continue to work well with increasing deployment density under the difficult requirements of flexibility and reconfigurability. Given that spectrum is a limited resource, this calls for disruptive technology innovation in the radio field. Cognitive radio systems (CRS) promise to be this disruptive technology innovation that will enable the future wireless world. A popular example of a CRS is an opportunistic radio, which temporally, spatially and geographically “re-uses” licensed spectrum. A typical example is that an “unlicensed” secondary user (SU) can be permitted to use licensed spectrum without causing harmful interference to any primary users (PU). In this way, the spectrum is used more efficiently. Although the theoretical research on CRS is blooming and has many interesting results, hardware design and system development are progressing at a much slower pace. This is because the complexity involved in designing and developing a CRS is so high. A key bottleneck in CRS has always been and continues to be a flexible radio frequency (RF) transceiver that consumes very low power and which can easily be coupled and integrated with the baseband processing of the cognitive radio. Another challenge is that cognitive algorithms and techniques should take into account the implementation constraints and requirements of CRS. Addressing these two main challenges has been the motivation of the CORPA project. In this project, we have proposed to design, develop, and demonstrate the receiver side of a CR platform that facilitates the realization of RF front-end functions for military and commercial communications systems. The CR receiver is more power-efficient and have wider bandwidth capability than current solutions. We have also proposed secure cooperative sensing algorithms, which are based on game theoretic approach. The flexible transceiver is based on an ADPLL-based polar transmitter/RFDAC and a sigma-delta receiver. The architecture enables flexibility based on a coarsely configurable front-end for matching, filtering, power and low-noise amplification, and a finely configurable signal path for conditioning and down conversion. To fully cover the range of 0.4 - 6 GHz, at least, 4 rows of such elements are included, covering 4 frequency bands: 400MHz-800MHz, 800MHz-1.6GHz, 1.6GHz-3.2GHz, and 3.2GHz-6GHz. This removes the need for custom-designed RF transceivers for each radio system and allows upgrades to future standards. We have proposed and designed a cognitive receiver, which is compliant with commercial systems such as GSM, WCDMA, LTE including LTE-A, and Wi-Fi as well as military communication equipment including GPS, border RADAR, and forward looking ground penetration RADAR. The cognitive receiver is a direct RF to digital delta sigma receiver covering a frequency range from 400 MHz to 6GHz. It is a 5th-order discrete-time delta sigma receiver and uses a 25% duty-cycle current-driven passive mixer and reconfigurable 2nd/4th passive/active loop filter. The prototype designed in 65 nm CMOS technology is configurable to operate over the wide frequency range of 400MHz to 6GHz, divided into three frequency bands: 400MHZ-800MHZ, 800MHz-1.6GHz, 1.6GHz-3.2GHz, and 3.2GHz-6GHz, operating at different center frequencies (0.4/0.85/1.2/2.1/2.4/3.6/4.0/6.0 GHz). The designed receiver achieves a noise figure of 9db at highest gain, out-of-band80 MHz IIP3 of 10 dBm and 65 dB of peak SNDR 10 dB better than the state-of-the-art. The Schreier factor of merit (Dynamic Range in dB + 10*log10(bandwidth/Power)) is 149 dB, which is better than the state-of-the-art. For secure cooperative sensing algorithms, to mitigate the primary user emulation attacker, who prevents other secondary users from accessing radio resources and interfere with nearby primary users, we have investigated the channel surveillance process to mitigate the selfish primary user emulation in the multi-channel attack context. By monitoring the occupied channels, the network manager can detect the selfish PUE attacker. Determining surveillance strategies, particularly in multi-channel context, is necessary for ensuring network operation fairness. Since a rational attacker can learn to adapt to the surveillance strategy, the question is how to formulate an appropriate modeling of the strategic interaction between a defender and an attacker. The relationship between the selfish PUE and the surveillance process are analyzed by game-theoretic approaches in the multi-channel attack context. We investigated two scenarios where the defender acts as a regular player (the non-commitment case), i.e., not commits to its surveillance strategy, or a leader (the commitment case), i.e., commits to its surveillance strategy and force the attacker to be a follower by play the best response regarding the observed strategy. In the commitment model, the network manager takes the leadership role by committing to its surveillance strategy and forces the attacker to follow the committed strategy. The relevant strategy is analyzed through the strong Stackelberg equilibrium. Compared with the non-commitment case analyzed through Nash equilibrium, analytical and numerical results show that the network manager significantly improves its utility with respect to playing a Nash equilibrium strategy, hence obtains a better protection against selfish PUEs. Moreover, the computational effort to compute the strong Stackelberg equilibrium strategy is lower than to find a Nash equilibrium strategy. We concluded that the defender must exploit the leader position in the game by committing its defense strategy to get the higher expected payoff. This method can be generalized to deal with the other types of PUE such as the malicious and the unknown attacking type ones.

Data: CORDIS, © European Union