Joint iterative beamforming and power adaptation for MIMO ad hoc networks

In this paper, we present distributed cooperative and regret-matching-based learning schemes for joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment. Under the total network power minimization criterion, a joint iterative approach is proposed to reduce the mutual interference at each node while ensuring a constant received signal-to-interference and noise ratio at each receiver. In cooperative and regret-matching-based power minimization algorithms, transmit beamformers are selected from a predefined codebook to minimize the total power. By selecting transmit beamformers judiciously and performing power adaptation, the cooperative algorithm is shown to converge to a pure strategy Nash equilibrium with high probability in the interference impaired network. The proposed cooperative and regret-matching-based distributed algorithms are also compared with centralized solutions through simulation results

Energy-Efficient Precoding for Multiple-Antenna Terminals

International audienceThe problem of energy-efficient precoding is investigated when the terminals in the system are equipped with multiple antennas. Considering static and fast-fading multiple-input multiple-output (MIMO) channels, the energy-efficiency is defined as the transmission rate to power ratio and shown to be maximized at low transmit power. The most interesting case is the one of slow fading MIMO channels. For this type of channels, the optimal precoding scheme is generally not trivial. Furthermore, using all the available transmit power is not always optimal in the sense of energy-efficiency (which, in this case, corresponds to the communication-theoretic definition of the goodput-to-power (GPR) ratio). Finding the optimal precoding matrices is shown to be a new open problem and is solved in several special cases: 1. when there is only one receive antenna; 2. in the low or high signal-to-noise ratio regime; 3. when uniform power allocation and the regime of large numbers of antennas are assumed. A complete numerical analysis is provided to illustrate the derived results and stated conjectures. In particular, the impact of the number of antennas on the energy-efficiency is assessed and shown to be significant

A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks

This paper proposes computationally efficient algorithms to maximize the energy efficiency in multi-carrier wireless interference networks, by a suitable allocation of the system radio resources, namely the transmit powers and subcarrier assignment. The problem is formulated as the maximization of the system Global Energy-Efficiency subject to both maximum power and minimum rate constraints. This leads to a challenging non-convex fractional problem, which is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and has a complexity linear in both the number of users and subcarriers, whereas other available solutions can only guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed method performs similarly as other, more complex, algorithms

On optimization of the resource allocation in multi-cell networks.

Chen, Jieying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 58-62).Abstract in English only.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Literature Review --- p.5Chapter 1.3 --- Contributions Of This Thesis --- p.7Chapter 1.4 --- Structure Of This Thesis --- p.8Chapter 2 --- Problem Formulation --- p.9Chapter 2.1 --- The JBAPC Problem --- p.9Chapter 2.2 --- The Single-Stage Reformulation --- p.12Chapter 3 --- The BARN Algorithm --- p.15Chapter 3.1 --- Preliminary Mathematics --- p.15Chapter 3.1.1 --- Duality Of The Linear Optimization Problem --- p.15Chapter 3.1.2 --- Benders Decomposition --- p.18Chapter 3.2 --- Solving The JBAPC Problem Using BARN Algorithm --- p.21Chapter 3.3 --- Performance And Convergence --- p.24Chapter 3.3.1 --- Global Convergence --- p.26Chapter 3.3.2 --- BARN With Error Tolerance --- p.26Chapter 3.3.3 --- Trade-off Between Performance And Convergence Time --- p.26Chapter 4 --- Accelerating BARN --- p.30Chapter 4.1 --- The Relaxed Master Problem --- p.30Chapter 4.2 --- The Feasibility Pump Method --- p.32Chapter 4.3 --- A-BARN Algorithm For Solving The JBAPC Problem --- p.34Chapter 5 --- Computational Results --- p.36Chapter 5.1 --- Global Optimality And Convergence --- p.36Chapter 5.2 --- Average Convergence Time --- p.37Chapter 5.3 --- Trade-off Between Performance And Convergence Time --- p.38Chapter 5.4 --- Average Algorithm Performance Of BARN and A-BARN --- p.39Chapter 6 --- Discussions --- p.47Chapter 6.1 --- Resource Allocation In The Uplink Multi-cell Networks --- p.47Chapter 6.2 --- JBAPC Problem In The Uplink Multi-cell Networks --- p.48Chapter 7 --- Conclusion --- p.50Chapter 7.1 --- Conclusion Of This Thesis --- p.50Chapter 7.2 --- Future Work --- p.51Chapter A --- The Proof --- p.52Chapter A.l --- Proof of Lemma 1 --- p.52Chapter A.2 --- Proof of Lemma 3 --- p.55Bibliography --- p.5

Transmitter Optimization in Multiuser Wireless Systems with Quality of Service Constraints

In this dissertation, transmitter adaptation for optimal resource allocation in wireless communication systems are investigated. First, a multiple access channel model is considered where many transmitters communicate with a single receiver. This scenario is a basic component of a. wireless network in which multiple users simultaneously access the resources of a wireless service provider. Adaptive algorithms for transmitter optimization to meet Quality-of-Service (QoS) requirements in a distributed manner are studied. Second, an interference channel model is considered where multiple interfering transmitter-receiver pairs co-exist such that a given transmitter communicates with its intended receiver in the presence of interference from other transmitters. This scenario models a wireless network in which several wireless service providers share the spectrum to offer their services by using dynamic spectrum access and cognitive radio (CR) technologies. The primary objective of dynamic spectrum access in the CR approach is to enable use of the frequency band dynamically and opportunistically without creating harmful interference to licensed incumbent users. Specifically, CR users are envisioned to be able to provide high bandwidth and efficient utilization of the spectrum via dynamic spectrum access in heterogeneous networks. In this scenario, a distributed method is investigated for combined precoder and power adaptation of CR transmitters for dynamic spectrum sharing in cognitive radio systems. Finally, the effect of limited feedback for transmitter optimization is analyzed where precoder adaptation uses the quantized version of interference information or the predictive vector quantization for incremental updates. The performance of the transmitter adaptation algorithms is also studied in the context of fading channels