In this paper, we present a low-latency direction assisted channel estimation algorithm suitable for millimeter wave (mm-Wave) systems. First, during the beam training procedure, we perform angle of departure (AoD) and angle of arrival (AoA) measurements with their corresponding variances and based on them, we design both downlink and uplink beams. We then perform signal measurements with these beams and accordingly design the sensing matrix, while also iteratively refining the angle estimation and the beams for the next measurements accordingly. Finally, exploiting both the sparseness and the intrinsic geometric nature of the mm-Wave channel, we apply compressive sensing tools so as to complete the estimation procedure. Simulations show that the corresponding estimation error decreases rapidly in comparison with other conventional approaches.

We study a millimeter wave (mm-wave) wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave small cell base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.

In this paper, we propose optimal beamforming strategies for a millimeter wave (mmWave) system consisting of multiple users based on the localization performance bounds. We consider a single base station (BS) with prior coarse knowledge of the users' positions and formulate the optimal beamforming problem in order to minimize the localization error consisting of Cramer Rao Lower Bounds (CRLBs) of delay, angle of departure (AoD) and angle of arrival (AoA) estimation at the mobile users. We first formulate the simplified CRLB of estimation parameters, taking advantage of multiple sub-carriers, and then formulate the localization error for optimization of the beamformer. Finally, we evaluate the resulting position and orientation error bounds after optimization for several fairness strategies through Monte Carlo simulations.

We analyze a millimeter wave network, deployed along the streets of a city, in terms of positioning and downlink data-rate performance, respectively. First, we present a transmission scheme where the base stations provide jointly positioning and data-communication functionalities. Accordingly, we study the trade- off between the localization and the data rate performance based on theoretical bounds. Then, we obtain an upper bound on the probability of beam misalignment based on the derived localization error bound. Finally, we prescribe the network operator a scheme to select the beamwidth and the power splitting factor between the localization and communication functions to address different quality of service requirements, while limiting cellular outage.

In this paper, we determine the optimal battery capacity for an energy harvesting wireless node. The goal is to achieve transmission performances similar to an identical system, but with infinitely large battery. In our previous work, addressing a node without any energy storage constraints, we proposed a policy to optimize instantaneous transmission rate in order to maximize the average transmission rate. Extending this idea we now minimize the capacity of the storage unit whilst maintaining the optimal instantaneous transmission rate as in the case with infinite battery capacity. A mathematical formulation to derive such optimal capacity is in turn presented and verified.