Rajeev Gangula

On the Value of Spectrum Sharing among Operators in Multicell Networks

This work considers the benefits of allowing spectrum sharing among co-located wireless service providers operating in the same multicell network. Although spectrum sharing was shown to be valuable in some scenarios where the created interference can be eliminated, the benefits have not clearly shown for multicell networks with aggressive reuse. We explore this question and show that spectrum sharing is preferred for just a certain subset of the users defined by their distance from the serving bases, while beyond this distance, an orthogonal division of resources between operators gives better results. The claims are backed with theoretical analysis matching our simulations.

Optimizing feedback in energy harvesting MISO communication channels

In this work,1 we consider the optimization of feedback in a point-to-point MISO channel with an energy harvesting (EH) receiver (RX). The RX is interested in feeding back the channel state to the transmitter (TX) to help improve the transmission rate, yet must spend the harvested energy wisely to do so. The objective is to maximize the throughput by a deadline, subject to EH constraints at the RX. The throughput metric considered is an upper bound on the ergodic capacity of beamforming with limited feedback. The optimization problem is shown to be concave and a simple algorithm for obtaining the optimal feedback bit allocation policy is devised. Numerical results show that the optimal feedback policy obtained for the modified problem outperforms the naive scheme for the original problem.

Distributed compression and transmission with energy harvesting sensors

We determine the achievable distortion region when the correlated source samples are transmitted by two energy harvesting (EH) sensor nodes to the destination over orthogonal fading channels. A time slotted system is considered in which the energy and the source samples arrive at the beginning of each time slot (TS), and both the correlation between source samples at the two nodes and fading coefficients change over time but remain constant in each TS. Assuming non-causal knowledge of these time-varying source statistics, energy arrivals and the channel gains, i.e., under the offline optimization framework, we obtain the optimal transmission and coding schemes that achieve the points on the Pareto boundary of the total distortion region. An iterative directional 2D waterfilling algorithm is proposed to obtain two specific points on this boundary.

Optimization of Energy Harvesting MISO Communication System With Feedback

Optimization of a point-to-point (p2p) multiple-input single-output (MISO) communication system is considered when both the transmitter (TX) and the receiver (RX) have energy harvesting (EH) capabilities. The RX is interested in feeding back the channel state information (CSI) to the TX to help improve the transmission rate. The objective is to maximize the throughput by a deadline, subject to the EH constraints at the TX and the RX. The throughput metric considered is an upper bound on the ergodic rate of the MISO channel with beamforming and limited feedback. Feedback bit allocation and transmission policies that maximize the upper bound on the ergodic rate are obtained. Tools from majorization theory are used to simplify the formulated optimization problems. Optimal policies obtained for the modified problem outperform the naive scheme in which no intelligent management of energy is performed.

Optimized data symbol allocation in multicell MIMO channels

In this work1, we consider the joint precoding across K distant transmitters (TXs) towards K single-antenna receivers (RXs). In practical networks, cooperation between TXs is limited by the constraints on the backhaul network and the common approach to limit the backhaul overhead is to form small disjoint clusters of cooperating TXs. Yet, this limits the performance due to interference at the cluster edge. We overcome this problem by directly optimizing the allocation of the users data symbol without clustering but solely subject to a constraint on the total number of symbols allocated. Since the problem of optimal data symbol allocation is of combinatorial nature, we use a greedy approach and develop greedy algorithms having low complexity while incuring only small losses compared to the optimal data symbol allocation. Moreover, the algorithms are shown to be Multiplexing Gain (MG) optimal in many settings. Simulations results confirm that our approach outperforms dynamic clustering methods from the literature.

A practical precoding scheme for multicell MIMO channels with partial user's data sharing

In this work1, we consider a setting where K Transmitters (TXs) equipped with multiple antennas aim at transmitting to their K respective Receivers (RXs) also equipped with multiple antennas. Without exchange of the users data symbols, this represents a conventional Interference Channel (IC), while it is a so-called MIMO Network Channel if the users data symbols are fully shared between all the TXs. The focus of this work is on the intermediate case where the users data symbols can be arbitrary shared to the TXs such that only a subset of the TXs has access to the data symbols to transmit to a given RX. We show that we can build a virtual IC so as so have the transmission in that IC equivalent to the transmission in the original setting. In this virtual IC, it is then possible to apply any of the numerous algorithms (Interference Alignment algorithms) initially tailored for the IC. Finally, we let the routing matrix be optimized subject to a constraint on the total number of symbols shared and use a greedy algorithm to find the users data allocation. We show by simulations that sharing only few users data symbols is sufficient to achieve most of the performance.