Distributed Power Control in Multiuser MIMO Networks with Optimal Linear Precoding

Contractive interference functions introduced by Feyzmahdavian et al. is the newest approach in the analysis and design of distributed power control laws. This approach can be extended to several cases of distributed power control. One of the distributed power control scenarios wherein the contractive interference functions have not been employed is the power control in MIMO systems. In this paper, this scenario will be analyzed. In addition, the optimal linear precoder is employed in each user to achieve maximum point-to-point information rate. In our approach, we use the same amount of signaling as the previous methods did. However, we show that the uniqueness of Nash equilibria is more probable in our approach, suggesting that our proposed method improves the convergence performance of distributed power control in MIMO systems. We also show that the proposed power control algorithm can be implemented asynchronously, which gives a noticeable flexibility to our algorithm given the practical communication limitations.Comment: 6 pages, 3 figures, Presented in 7th International Symposium on Telecommunications (IST 2014

Distributed Bargaining Mechanisms for Multi-antenna Dynamic Spectrum Access Systems

Abstract-Dynamic spectrum access and MIMO technologies are among the most promising solutions to address the ever increasing wireless traffic demand. An integration that successfully embraces the two is far from trivial due to the dynamics of spectrum opportunities as well as the requirement to jointly optimize both spectrum and spatial/antenna dimensions. Our objective in this paper is to jointly allocate opportunistic channels to various links such that no channel is allocated to more than one link, and to simultaneously optimize the MIMO precoding matrices under the Nash bargaining (NB) framework. We design a low-complexity distributed scheme that allows links to propose their minimum rate requirements, negotiate the channel allocation, and configure their precoding matrices. Simulations confirm the convergence of the distributed algorithm under timesharing to the globally optimal solution of the NB-based problem. They also show that the NB-based algorithm achieves much better fairness than purely maximizing network throughput

Heterogeneous Spectrum Sharing with Rate Demands in Cognitive MIMO Networks

Abstract-We are interested in addressing a fundamental question: what are conditions under which an ad hoc cognitive radio MIMO (CMIMO) network can support a given rate-demand profile, defined as the set of rates requested by individual links? From an information theoretic view, a rate profile can be supported if it is within the network capacity region. However, the network capacity region of interfering MIMO networks is essentially unknown. In dynamic spectrum access, the problem is even more challenging due to the dynamics of primary/legacy users (PUs), resource constraints, and the heterogeneity of opportunistic spectrum (i.e., the set of available channels varies from one to another). Considering a non-centralized setup, we address the above question in a noncooperative game framework where each CMIMO link independently optimizes its spectrum, power allocation, and MIMO precoders to meet its rate demand. We derive sufficient conditions for the existence of a NE are derived. These conditions establish an explicit relationship between the rate-demand profile and interference from PUs, CMIMO network's interference, and CMIMO nodes' power budget. We also show that a NE, if exists, is unique. Our results help to characterize the network capacity region of CMIMO networks

Sensitivity and Asymptotic Analysis of Inter-Cell Interference Against Pricing for Multi-Antenna Base Stations

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism

Game theory for collaboration in future networks

Cooperative strategies have the great potential of improving network performance and spectrum utilization in future networking environments. This new paradigm in terms of network management, however, requires a novel design and analysis framework targeting a highly flexible networking solution with a distributed architecture. Game Theory is very suitable for this task, since it is a comprehensive mathematical tool for modeling the highly complex interactions among distributed and intelligent decision makers. In this way, the more convenient management policies for the diverse players (e.g. content providers, cloud providers, home providers, brokers, network providers or users) should be found to optimize the performance of the overall network infrastructure. The authors discuss in this chapter several Game Theory models/concepts that are highly relevant for enabling collaboration among the diverse players, using different ways to incentivize it, namely through pricing or reputation. In addition, the authors highlight several related open problems, such as the lack of proper models for dynamic and incomplete information games in this area.info:eu-repo/semantics/acceptedVersio

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

Single carrier frequency domain equalization and energy efficiency optimization for MIMO cognitive radio.

This dissertation studies two separate topics in wireless communication systems. One topic focuses on the Single Carrier Frequency Domain Equalization (SC-FDE), which is a promising technique to mitigate the multipath effect in the broadband wireless communication. Another topic targets on the energy efficiency optimization in a multiple input multiple output (MIMO) cognitive radio network. For SC-FDE, the conventional linear receivers suffer from the noise amplification in deep fading channel. To overcome this, a fractional spaced frequency domain (FSFD) receiver based on frequency domain oversampling (FDO) is proposed for SC-FDE to improve the performance of the linear receiver under deep fading channels. By properly designing the guard interval, a larger sized Discrete Fourier Transform (DFT) is equipped to oversample the received signal in frequency domain. Thus, the effect of frequency-selective fading can still be eliminated by a one-tap frequency domain filter. Two types of FSFD receivers are proposed based on the least square (LS) and minimum mean square error (MMSE) criterion. Both the semi-analytical analysis and simulation results are given to evaluate the performance of the proposed receivers. Another challenge in SC-FDE is the in-phase/quadrature phase (IQ) imbalance caused by unideal radio frequency (RF) front-end at the transmitter or the receiver. Most existing works in single carrier transmission employ linear compensation methods, such as LS and MMSE, to combat the interference caused by IQ imbalance. Actually, for single carrier transmissions, it is possible for the receivers to adopt advanced nonlinear compensation methods to improve the system performance under IQ imbalance. For such purpose, an iterative decision feedback receiver is proposed to compensate the IQ imbalance caused by unideal RF front-end in SC-FDE. Numerical results show that the proposed iterative IQ imbalance compensation can significantly improve the performance of SC-FDE system under IQ imbalance compared with the conventional linear method. For the energy efficiency optimization in the MIMO cognitive radio network, multiple secondary users (SUs) coexisting with a primary user (PU) adjust their antenna radiation patterns and power allocations to achieve energy-efficient transmission. The optimization problems are formulated to maximize the energy efficiency of a cognitive radio network in both distributed and centralized point of views. Also, constraints on the transmission power and the interference to PU are introduced to protect the PU’s transmission. In order to solve the non-convex optimization problems, convex relaxations are used to transform them into equivalent problems with better tractability. Then three optimization algorithms are proposed to find the energy-efficient transmission strategies. Simulation results show that the proposed energy-efficiency optimization algorithms outperform the existing algorithms

Price-based joint beamforming and spectrum management in multi-antenna cognitive radio networks

We consider the problem of maximizing the throughput of a multi-antenna cognitive radio (CR) network. With spatial multiplexing over each frequency band, a multi-antenna CR node controls its antenna radiation directions and allocates power for each data stream by appropriately adjusting its precoding matrix. Our objective is to design a set of precoding matrices (one per band) at each CR node so that power and spectrum are optimally allocated for the node and its interference is steered away from unintended receivers. The problem is non-convex, with the number of variables growing quadratically with the number of antenna elements. To tackle it, we translate it into a noncooperative game. We derive an optimal pricing policy for each node, which adapts to the node's neighboring conditions and drives the game to a Nash-Equilibrium (NE). The network throughput under this NE equals to that of a locally optimal solution of the non-convex centralized problem. To find the set of precoding matrices at each node (best response), we develop a low-complexity distributed algorithm by exploiting the strong duality of the convex per-user optimization problem. The number of variables in the distributed algorithm is independent of the number of antenna elements. A centralized (cooperative) algorithm is also developed. Simulations show that the network throughput under the distributed algorithm rapidly converges to that of the centralized one. Finally, we develop a MAC protocol that implements our resource allocation and beamforming scheme. Extensive simulations show that the proposed protocol dramatically improves the network throughput and reduces power consumption. © 1983-2012 IEEE