The deployment of small cell base stations (SCBSs) overlaid on existing macro-cellular systems is seen as a key solution for offloading traffic, optimizing coverage, and boosting the capacity of future cellular wireless systems. The nextgeneration of SCBSs is envisioned to be multi-mode, i.e., capable of transmitting simultaneously on both licensed and unlicensed bands. This constitutes a cost-effective integration of both WiFi and cellular radio access technologies (RATs) that can efficiently cope with peak wireless data traffic and heterogeneous qualityof-service requirements. To leverage the advantage of such multimode SCBSs, we discuss the novel proposed paradigm of crosssystem learning by means of which SCBSs self-organize and autonomously steer their traffic flows across different RATs. Cross-system learning allows the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For example, the SCBSs can offload delay-tolerant data traffic to WiFi, while simultaneously learning the probability distribution function of their transmission strategy over the licensed cellular band. This article will first introduce the basic building blocks of cross-system learning and then provide preliminary performance evaluation in a Long-Term Evolution (LTE) simulator overlaid with WiFi hotspots. Remarkably, it is shown that the proposed cross-system learning approach significantly outperforms a number of benchmark traffic steering policies.

capacity of a measured broadband radio channel is calculated An OFDM (orthogonal frequency division multiplexing) transmission system is simulated with time-variant transfer functions measured with a wideband channel sounder. The individual subcarriers are modulated with fixed and adaptive signal alphabets. Furthermore, a frequency-independent as well as the optimum power distribution are used. The simulations show that with adaptive OFDM, the required signal power for an error probability of can be reduced by 5 ... 15 dB compared with fixed OFDM. The fraction of channel capacity which can be achieved with adaptive OFDM depends on the average signal-to-noise ratio and the propagation scenario.

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The aim of the present paper is to compare multicarrier and single carrier modulation schemes for radio communication systems. In both cases the fast Fourier transform (FFT) and its inverse are utilized. In case of OFDM (orthogonal frequency division multiplexing), the inverse FFT transforms the complex amplitudes of the individual subcarriers at the transmitter into time domain. At the receiver the inverse operation is carried out. In case of single carrier modulation, the FFT and its inverse are used at the input and output of the frequency domain equalizer in the receiver. Different single carrier and multicarrier transmission systems are simulated with time-variant transfer functionsmeasured with a wideband channel sounder. In case of OFDM, the individual subcarriers are modulated with fixed and adaptive signal alphabets. Furthermore, a frequency-independent as well as the optimum power distribution are used.

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In this paper, we investigate enhanced Inter-Cell Interference Coordination (e-ICIC) techniques for Heterogeneous Networks (HetNets), consisting of a mix of macro and picocells.We model this strategic coexistence as a multi-agent system in which decentralized interference management and cell associa tion strategies inspired from Reinforcement Learning (RL) are devised. Specifically, we focus on time and frequency domain ICIC techniques in which picocells optimally learn their cell range bias and downlink transmit power allocation. In turn, the macrocell optimizes its transmission by serving its own users while adhering to the picocell interference constraint. To substantiate our theoretical findings, system level simulations are carried out in which our proposed solution is compared with a number of existing ICIC approaches, such as resource partitioning, fixed cell range expansion (CRE) and fixed Almost Blank Subframe (ABS). Interestingly, our proposed solution is shown to yield substantial gains of up to 125% compared to static ICIC approaches.

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In this paper, we propose a fast terahertz time-domain imaging method using a radar migration algorithm. We demonstrate high-resolution imaging in reflection without any collimating or focusing optics in the terahertz beam. In the proposed method, the sample is illuminated with a divergent terahertz beam, and the receiver collects both specular and diffuse reflections. We further present calibration and post-processing methods that allow us to compensate for the inherently low signal-to-noise ratio of an unfocused terahertz beam. The feasibility of the novel imaging method is demonstrated with geometrically complex samples and a fast terahertz time-domain spectroscopy system based on electronically controlled optical sampling. We show that our concept is capable of generating images of the objects regardless of their size, shape, orientation and position relative to the transmitter and receiver antennas. Objects with edge lengths well below 400 µm can be clearly detected. The method presented here thus lends itself to arbitrary scenarios and antenna configurations. INDEX TERMSTerahertz time-domain spectroscopy, lensless terahertz imaging, ECOPS, radar migration algorithms.