Criteria on Utility Designing of Convex Optimization in FDMA Networks

In this paper, we investigate the network utility maximization problem in FDMA systems. We summarize with a suite of criteria on designing utility functions so as to achieve the global optimization convex. After proposing the general form of the utility functions, we present examples of commonly used utility function forms that are consistent with the criteria proposed in this paper, which include the well-known proportional fairness function and the sigmoidal-like functions. In the second part of this paper, we use numerical results to demonstrate a case study based on the criteria mentioned above, which deals with the subcarrier scheduling problem with dynamic rate allocation in FDMA system

Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex optimization for signal processin

Distributed Algorithms for the Optimal Design of Wireless Networks

This thesis studies the problem of optimal design of wireless networks whose operating points such as powers, routes and channel capacities are solutions for an optimization problem. Different from existing work that rely on global channel state information (CSI), we focus on distributed algorithms for the optimal wireless networks where terminals only have access to locally available CSI. To begin with, we study random access channels where terminals acquire instantaneous local CSI but do not know the probability distribution of the channel. We develop adaptive scheduling and power control algorithms and show that the proposed algorithm almost surely maximizes a proportional fair utility while adhering to instantaneous and average power constraints. Then, these results are extended to random access multihop wireless networks. In this case, the associated optimization problem is neither convex nor amenable to distributed implementation, so a problem approximation is introduced which allows us to decompose it into local subproblems in the dual domain. The solution method based on stochastic subgradient descent leads to an architecture composed of layers and layer interfaces. With limited amount of message passing among terminals and small computational cost, the proposed algorithm converges almost surely in an ergodic sense. Next, we study the optimal transmission over wireless channels with imperfect CSI available at the transmitter side. To reduce the likelihood of packet losses due to the mismatch between channel estimates and actual channel values, a backoff function is introduced to enforce the selection of more conservative coding modes. Joint determination of optimal power allocations and backoff functions is a nonconvex stochastic optimization problem with infinitely many variables. Exploiting the resulting equivalence between primal and dual problems, we show that optimal power allocations and channel backoff functions are uniquely determined by optimal dual variables and develop algorithms to find the optimal solution. Finally, we study the optimal design of wireless network from a game theoretical perspective. In particular, we formulate the problem as a Bayesian game in which each terminal maximizes the expected utility based on its belief about the network state. We show that optimal solutions for two special cases, namely FDMA and RA, are equilibrium points of the game. Therefore, the proposed game theoretic formulation can be regarded as general framework for optimal design of wireless networks. Furthermore, cognitive access algorithms are developed to find solutions to the game approximately

Power Allocation in Wireless Relay Networks

This thesis is mainly concerned with power allocation issues in wireless relay networks where a single or multiple relays assist transmission from a single or multiple sources to a destination. First, a network model with a single source and multiple relays is considered, in which both cases of orthogonal and non--orthogonal relaying are investigated. For the case of orthogonal relaying, two power allocation schemes corresponding to two partial channel state information (CSI) assumptions are proposed. Given the lack of full and perfect CSI, appropriate signal processing at the relays and/or destination is also developed. The performance behavior of the system with power allocation between the source and the relays is also analyzed. For the case of non-orthogonal relaying, it is demonstrated that optimal power allocation is not sufficiently effective. Instead, a relay beamforming scheme is proposed. A comprehensive comparison between the orthogonal relaying with power allocation scheme and the non-orthogonal relaying with beamforming scheme is then carried out, which reveals several interesting conclusions with respect to both error performance and system throughput. In the second part of the thesis, a network model with multiple sources and a single relay is considered. The transmission model is applicable for uplink channels in cellular mobile systems in which multiple mobile terminals communicate with the base station with the help of a single relay station. Single-carrier frequency division multiple access (SC-FDMA) technique with frequency domain equalization is adopted in order to avoid the amplification of the multiple access interference at the relay. Minimizing the transmit power at the relay and optimizing the fairness among the sources in terms of throughput are the two objectives considered in implementing power allocation schemes. The problems are visualized as water-filling and water-discharging models and two optimal power allocation schemes are proposed, accordingly. Finally, the last part of the thesis is extended to a network model with multiple sources and multiple relays. The orthogonal multiple access technique is employed in order to avoid multiple access interference. Proposed is a joint optimal beamforming and power allocation scheme in which an alternative optimization technique is applied to deal with the non-convexity of the power allocation problem. Furthermore, recognizing the high complexity and large overhead information exchange when the number of sources and relays increases, a relay selection scheme is proposed. Since each source is supported by at most one relay, the feedback information from the destination to each relay can be significantly reduced. Using an equal power allocation scheme, relay selection is still an NP-hard combinatorial optimization problem. Nevertheless, the proposed sub-optimal scheme yields a comparable performance with a much lower computational complexity and can be well suited for practical systems

Review of Optimization Problems in Wireless Sensor Networks

International audienc

Resource Allocation for Multiple-Input and Multiple-Output Interference Networks

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

Spectrally and Energy Efficient Radio Resource Management for Multi-Operator Shared Networks

Commercial mobile communication systems are mainly based on licensed frequency spectrum, and the license is very expensive as the spectrum is a sparse wireless resource. Therefore, sharing this wireless resource is an essential requirement not only at the present but also in the future considering trends like connectivity for everybody and everything. In this thesis, we study the sharing of wireless resources with different approaches for realizing fair, efficient, and predictable sharing solutions in a controlled manner. The efficient use of wireless channel resources is an important target to reduce the costs of network operation and deployment. To achieve this, we need practical scheduling algorithms for wireless resources, out of which several of them will be presented and analyzed in this work. Different optimization frameworks for the spectral efficiency utility are presented, with an individual focus on guaranteeing resource or rate fairness among the operators in a network with shared radio resources. Thus, the presented proposals will help the mobile network operators to overcome the issues of losing network control and traceability of used wireless resources in a shared environment. Besides this, emerging vertical industries, such as automotive, healthcare, industry 4.0, internet of things (IoT) industries will put a certain burden on the wireless networks asking for guaranteed service level requirement from the mobile network operators. In this regard, this thesis provides the necessary methods addressing these challenges with the help of scheduling methods which are based on the joint optimization of spectral and energy efficiency. Thus, wireless networks will be enabled as a service function in a controlled and scalable way for new emerging markets. Furthermore, the presented solutions t well with the requirements of fifth generation (5G) network slicing

Mathematical optimization and game theoretic techniques for multicell beamforming

The main challenge in mobile wireless communications is the incompatibility between limited wireless resources and increasing demand on wireless services. The employment of frequency reuse technique has effectively increased the capacity of the network and improved the efficiency of frequency utilization. However, with the emergence of smart phones and even more data hungry applications such as interactive multimedia, higher data rate is demanded by mobile users. On the other hand, the interference induced by spectrum sharing arrangement has severely degraded the quality of service for users and restricted further reduction of cell size and enhancement of frequency reuse factor. Beamforming technique has great potential to improve the network performance. With the employment of multiple antennas, a base station is capable of directionally transmitting signals to desired users through narrow beams rather than omnidirectional waves. This will result users suffer less interference from the signals transmitted to other co-channel users. In addition, with the combination of beamforming technique and appropriate power control schemes, the resources of the wireless networks can be used more efficiently. In this thesis, mathematical optimization and game theoretic techniques have been exploited for beamforming designs within the context of multicell wireless networks. Both the coordinated beamforming and the coalitional game theoretic based beamforming techniques have been proposed. Initially, coordinated multicell beamforming algorithms for mixed design criteria have been developed, in which some users are allowed to achieve target signal-to-interference- plus-noise ratios (SINRs) while the SINRs of rest of the users in all cells will be balanced to a maximum achievable SINR. An SINR balancing based coordinated multicell beamforming algorithm has then been proposed which is capable of balancing users in different cells to different SINR levels. Finally, a coalitional game based multicell beamforming has been considered, in which the proposed coalition formation algorithm can reach to stable coalition structures. The performances of all the proposed algorithms have been demonstrated using MATLAB based simulations