We propose projecting radar waveform onto the null space of an interference channel matrix between the radar and a communication system as a solution for coexistence of radar and communication systems in the same band. This approach assumes that the cognitive radar has full knowledge of the interference channel and tries to modify its signal vectors in such a way that they fall in the null space of the channel matrix. We investigate the effects of null space projections on radar performance and target parameter identification both analytically and quantita tively by using maximum likelihood and Cramer-Rao bound performance bounds to estimate target direction in the two cases of no null space projection and null space projection. Through simulation we demonstrate that by optimal choice of the number of antennas, the performance and target identification capabilities of radar in our method are competitive with that of traditional radar waveforms, while simultaneously guaranteeing coexistence between radar and communication systems.

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The demand for spectrum usage is increased which requires new spectrum allotments. For the coexistence of wireless system with the radar systems a dynamic allocation of spectrum method is proposed along with the noise power the communication system power is considered as interference power to the radar system. The communication power effect on radar system as a function of distance is analysed .A multiple input multiple output radar system and MIMO wireless communication system with K base stations are considered. The communication system transmit covariance matrix is designed based on the radar sampling scheme to reduce the effective interference power (EIP) for radar receiver by certain average capacity and transmit power maintained for communication system

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Spectrum sharing is a promising solution for the problem of spectrum congestion. We consider a spectrum sharing scenario between a multiple-input multiple-output (MIMO) radar and Long Term Evolution (LTE) Advanced cellular system. In this paper, we consider resource allocation optimization problem with carrier aggregation. The LTE Advanced system has N BS base stations (BS) which it operates in the radar band on a sharing basis. Our objective is to allocate resources from the LTE Advanced carrier and the MIMO radar carrier to each user equipment (UE) in an LTE Advanced cell based on the running application of UE. Each user application is assigned a utility function based on the type of application. We propose a carrier aggregation resource allocation algorithm to allocate the LTE Advanced and the radar carriers' resources optimally among users based on the type of user application. The algorithm gives priority to users running inelastic traffic when allocating resources. Finally we present simulation results on the performance of the proposed carrier aggregation resource allocation algorithm.

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In this paper, we introduce a novel approach for power allocation in cellular networks. In our model, we use sigmoidal-like utility functions to represent different users' modulation schemes. Each utility function is a representation of the probability of successfully transmitted packets per unit of power consumed by a user, when using a certain modulation scheme. We consider power allocation with utility proportional fairness policy, where the fairness among users is in utility percentage i.e. percentage of successfully transmitted packets of the corresponding modulation scheme. We formulate our network utility maximization problem as a product of utilities of all users and prove that our power allocation optimization problem is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate base station (BS) powers optimally with priority given to users running lower modulation schemes while ensuring non-zero power allocation to users running higher modulation schemes.Our algorithm prevents fluctuation in the power allocation process and is capable of traffic and modulation dependent pricing i.e. charges different price per unit power from different users depending in part on their modulation scheme and total power available at the BS. This is used to flatten traffic and decrease the service price for users.

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