2,397 research outputs found

    Jointly Optimal Channel and Power Assignment for Dual-Hop Multi-channel Multi-user Relaying

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    We consider the problem of jointly optimizing channel pairing, channel-user assignment, and power allocation, to maximize the weighted sum-rate, in a single-relay cooperative system with multiple channels and multiple users. Common relaying strategies are considered, and transmission power constraints are imposed on both individual transmitters and the aggregate over all transmitters. The joint optimization problem naturally leads to a mixed-integer program. Despite the general expectation that such problems are intractable, we construct an efficient algorithm to find an optimal solution, which incurs computational complexity that is polynomial in the number of channels and the number of users. We further demonstrate through numerical experiments that the jointly optimal solution can significantly improve system performance over its suboptimal alternatives.Comment: This is the full version of a paper to appear in the IEEE Journal on Selected Areas in Communications, Special Issue on Cooperative Networking - Challenges and Applications (Part II), October 201

    Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks

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    In this paper, we examine the fundamental trade-off between radiated power and achieved throughput in wireless multi-carrier, multiple-input and multiple-output (MIMO) systems that vary with time in an unpredictable fashion (e.g. due to changes in the wireless medium or the users' QoS requirements). Contrary to the static/stationary channel regime, there is no optimal power allocation profile to target (either static or in the mean), so the system's users must adapt to changes in the environment "on the fly", without being able to predict the system's evolution ahead of time. In this dynamic context, we formulate the users' power/throughput trade-off as an online optimization problem and we provide a matrix exponential learning algorithm that leads to no regret - i.e. the proposed transmit policy is asymptotically optimal in hindsight, irrespective of how the system evolves over time. Furthermore, we also examine the robustness of the proposed algorithm under imperfect channel state information (CSI) and we show that it retains its regret minimization properties under very mild conditions on the measurement noise statistics. As a result, users are able to track the evolution of their individually optimum transmit profiles remarkably well, even under rapidly changing network conditions and high uncertainty. Our theoretical analysis is validated by extensive numerical simulations corresponding to a realistic network deployment and providing further insights in the practical implementation aspects of the proposed algorithm.Comment: 25 pages, 4 figure

    Electromagnetic emission-aware schedulers for the uplink of OFDM wireless communication systems

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    The popularity and convergence of wireless communications have resulted in continuous network upgrades in order to support the increasing demand for bandwidth. However, given that wireless communication systems operate on radiofrequency waves, the health effects of electromagnetic emission from these systems are increasingly becoming a concern due to the ubiquity of mobile communication devices. In order to address these concerns, we propose two schemes (offline and online) for minimizing the EM emission of users in the uplink of OFDM systems, while maintaining an acceptable quality of service. We formulate our offline EM reduction scheme as a convex optimization problem and solve it through water-filling. This is based on the assumption that the long-term channel state information of all the users is known. Given that, in practice, long-term channel state information of all the users cannot always be available, we propose our online EM emission reduction scheme, which is based on minimizing the instantaneous transmit energy per bit of each user. Simulation results show that both our proposed schemes significantly minimize the EM emission when compared to the benchmark classic greedy spectral efficiency based scheme and an energy efficiency based scheme. Furthermore, our offline scheme proves to be very robust against channel prediction errors
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