Millimeter-wave transceivers use large antenna arrays to form narrow high-directional beams and overcome severe attenuation. Narrow beams require large signaling overhead to be aligned if no prior information about beam directions is available. Moreover, beams drift with time due to user mobility and may need to be realigned. Beam tracking is commonly used to keep the beams tightly coupled and eliminate the overhead associated with realignment. Hence, with periodic measurements, beams are adjusted before they lose alignment. We propose a model where the receiver adjusts beam direction “continuously” over each physical-layer sample according to a carefully calculated estimate of the continuous variation of the beams. In our approach, the change of direction is updated using the rate variation prediction of beam angles via three different solutions. Our approach incurs no additional overhead in pilots, yet, the performance of beam tracking is improved significantly. Numerical results reveal an SNR enhancement associated with reducing the MSE of the beam directions. In addition, our approach reduces the pilot overhead by 60% and up to 87% while achieving a similar total tracking duration as the state-of-the-art.

Large MIMO transceivers are integral components of next-generation wireless networks. However, for such systems to be practical, their channel estimation process needs to be fast and reliable. Although several solutions for fast estimation of sparse channels do exist, there is still a gap in understanding the fundamental limits governing this problem. Specifically, we need to better understand the lower bound on the number of measurements under which accurate channel estimates can be obtained. This work bridges that knowledge gap by deriving a tight asymptotic lower bound on the number of measurements. This not only helps develop a better understanding for the sparse MIMO channel estimation problem, but it also provides a benchmark for evaluating current and future solutions.

The speed at which millimeter-Wave (mmWave) channel estimation can be carried out is critical for the adoption of mmWave technologies. This is particularly crucial because mmWave transceivers are equipped with large antenna arrays to combat severe path losses, which consequently creates large channel matrices, whose estimation may incur significant overhead. This paper focuses on the mmWave channel estimation problem. Our objective is to reduce the number of measurements required to reliably estimate the channel. Specifically, channel estimation is posed as a “source compression” problem in which measurements mimic an encoded (compressed) version of the channel. Decoding the observed measurements, a task which is traditionally computationally intensive, is performed using a deep-learning-based approach, facilitating a high-performance channel discovery. Our solution not only outperforms state-of-the-art compressed sensing methods, but it also determines the lower bound on the number of measurements required for reliable channel discovery.

The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This paper addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.

The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This work addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.

Opportunistic spectrum access is a propitious technique to overcome the under-utilization of spectrum bands. In this work, we design an experimental test-bed for evaluating an un-slotted spectrum access scheme under real indoor environment conditions. To this end, we use the USRP software defined radio platform along with the GNURadio software that incorporates the PHY and MAC functions and modules. Our contribution is multi-fold. First, we design a MAC protocol to integrate the packet based transmission of the coexisting PU/SU network, while compensating for spectrum sensing imperfection as well as collision detection faults. Second, we evaluate the USRP-induced latency (delay) and show that it has random behavior. We work around it to obtain a fixed packet transmission time which is crucial for the channel access scheme realization and evaluation. Third, we perform helping experiments to quantify the spectrum sensing imperfection in terms of false alarm and detection probabilities. We also quantify the imperfection in collision detection. Finally, we evaluate the performance of the whole channel access scheme and compare its results to the classical sense-transmit scheme. We show that 28.5% increase in SU throughput can be achieved for the same PU packet collision rate.

In this paper we study the problem of secondary user channel access in cognitive radio networks. In particular, we address the problem of deciding the secondary user sensing vs. transmission at any point of time, assuming the availability of the primary user idle time statistics. Towards this objective, we make the following contributions. First, unlike prior work, we assume unconstrained general idle time distribution for the primary user under secondary user imperfect sensing and imperfect collision detection. Second, we propose a novel approach grounded in reliability theory to analyze the time based activity of the primary user. Finally, motivated by the sheer complexity of the problem, we propose three heuristic schemes for deciding the secondary user sensing/transmission actions at any point of time. We conduct computer simulations to evaluate the performance of the proposed schemes and compare them to the traditional per-packet sensing/transmission scheme and to each other. Our numerical results reveal that, for an experimentally verified idle time distribution in heterogeneous network activity, at least one of our proposed schemes can achieve 27% throughput increase and down to 3.85%, under different QoS requirements for the PU. Also we show through simulations that our heuristic schemes are very close to optimal, when the optimal scheme can be applied.