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The millimeter wave (mmWave) band offers vast bandwidth and plays a key role for next generation wireless networks. However, the mmWave network raises a great challenge for user association and scheduling , due to the limited power budget and beamformers, diverse user traffic loads, user quality of service requirement, etc. In this paper, we propose a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink UE assOciation, Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time, subject to the base station power budget, number of beamformers, user traffic loads, and the quality of service requirement at users. We compare BOOST with three state-of-the-art user scheduling schemes. On average, BOOST reduces the transmission time by 37, 30, and 26 percent, and achieves a sum rate gain of 56, 43, and 34 percent, respectively.

The millimeter wave (mmWave) frequency band is a promising candidate for next generation cellular and wireless networks. To compensate the significantly higher path loss due to the higher frequency, the mmWave band usually uses the beamforming technology. However, this makes the network topology control a great challenge. In this paper, we propose a novel framework for network topology control in mmWave networks, termed Beamforming Oriented tOpology coNtrol (BOON). The objective is to reduce total transmit power of base stations and interference between beams. BOON smartly groups nearby user equipment into clusters, constructs sets from user equipment clusters, and associates user equipment to base stations and beams. We compare BOON with three existing topology control schemes in terms of transmit power, network sum rate, signal to interference and noise ratio, and computation complexity. The results indicate that overall BOON significantly outperforms them. In particular, on average BOON uses only 10, 32, and 25 percent transmit power of other three schemes, respectively, to achieve the same network sum rate.

Spectrum sharing is a key technology to relieve the ever-increasing spectrum demand and realize the full potential of radio spectrum. In this paper, we study spectrum sharing between higher priority users and lower priority users under geo-location based spectrum sharing systems. We consider a dynamic spectrum allocation scheme that allocates spectrum to a higher priority user based on its spectrum need that can be determined by its traffic load. The lower priority users utilize the unallocated spectrum. In addition to studying the performance of higher priority users with this dynamic spectrum allocation scheme, we also investigate the impact of this scheme on spectrum availability and stability of lower priority users. We develop a mathematical model to analyze the performance. The simulation results indicate that spectrum sharing is efficient, and the spectrum is abundant and relatively stable to lower priority users, even when the system is moderately loaded with higher priority users.

Spectrum sensing plays a critical role in cognitive radio networks. Most of existing works on spectrum sensing adopted energy detection which takes samples on a band and then compares the summation with a threshold to determine the state of the band. However, if a licensed band is subdivided by the primary users, such as in the unlicensed WiFi band, the energy detection faces a challenge. The threshold used to decide if there is a PU signal on the band now depends on the number of sub-bands that are being used by primary users, since the received signal power on the band is now dependent on the number of used sub-bands. In this work, we propose a wavelet based spectrum sensing approach that does not depend on the number of used sub-bands and adaptively detects PU signals on a licensed band. We use the measured real world signals to test the approach. The simulation results indicate that the proposed approach can effectively detect the PU signal on a licensed band without needing the knowledge of band subdivision. In addition, the comparative study with the existing techniques is performed to evaluate two performance metrics, true detection and false alarm, for primary users signal detection.