Scheduling for Multi-Camera Surveillance in LTE Networks

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while guaranteeing the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure

Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks

In this paper, we study the resource allocation and user scheduling problem for a downlink nonorthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.Comment: Accepted as a regular paper by IEEE Transactions on Wireless Communication

Matrix columns allocation problems

AbstractOrthogonal Frequency Division Multiple Access (OFDMA) transmission technique is gaining popularity as a preferred technique in the emerging broadband wireless access standards. Motivated by the OFDMA transmission technique we define the following problem: Let M be a matrix (over R) of size a×b. Given a vector of non-negative integers C→=〈c1,c2,…,cb〉 such that ∑cj=a, we would like to allocate a cells in M such that (i) in each row of M there is a single allocation, and (ii) for each element ci∈C→ there is a unique column in M which contains exactly ci allocations. Our goal is to find an allocation with minimal value, that is, the sum of all the a cells of M which were allocated is minimal. The nature of the suggested new problem is investigated in this paper. Efficient algorithms are suggested for some interesting cases. For other cases of the problem, NP-hardness proofs are given followed by inapproximability results

Studies on efficient spectrum sharing in coexisting wireless networks.

Wireless communication is facing serious challenges worldwide: the severe spectrum shortage along with the explosive increase of the wireless communication demands. Moreover, different communication networks may coexist in the same geographical area. By allowing multiple communication networks cooperatively or opportunistically sharing the same frequency will potentially enhance the spectrum efficiency. This dissertation aims to investigate important spectrum sharing schemes for coexisting networks. For coexisting networks operating in interweave cognitive radio mode, most existing works focus on the secondary network’s spectrum sensing and accessing schemes. However, the primary network can be selfish and tends to use up all the frequency resource. In this dissertation, a novel optimization scheme is proposed to let primary network maximally release unnecessary frequency resource for secondary networks. The optimization problems are formulated for both uplink and downlink orthogonal frequency-division multiple access (OFDMA)-based primary networks, and near optimal algorithms are proposed as well. For coexisting networks in the underlay cognitive radio mode, this work focuses on the resource allocation in distributed secondary networks as long as the primary network’s rate constraint can be met. Global optimal multicarrier discrete distributed (MCDD) algorithm and suboptimal Gibbs sampler based Lagrangian algorithm (GSLA) are proposed to solve the problem distributively. Regarding to the dirty paper coding (DPC)-based system where multiple networks share the common transmitter, this dissertation focuses on its fundamental performance analysis from information theoretic point of view. Time division multiple access (TDMA) as an orthogonal frequency sharing scheme is also investigated for comparison purpose. Specifically, the delay sensitive quality of service (QoS) requirements are incorporated by considering effective capacity in fast fading and outage capacity in slow fading. The performance metrics in low signal to noise ratio (SNR) regime and high SNR regime are obtained in closed forms followed by the detailed performance analysis

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Workflow Optimization for Parallel Split Learning

Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper. In parallel SL, multiple helpers can process model parts of one or more clients, thus, considerably reducing the maximum training time over all clients (makespan). In this paper, we focus on orchestrating the workflow of this operation, which is critical in highly heterogeneous systems, as our experiments show. In particular, we formulate the joint problem of client-helper assignments and scheduling decisions with the goal of minimizing the training makespan, and we prove that it is NP-hard. We propose a solution method based on the decomposition of the problem by leveraging its inherent symmetry, and a second one that is fully scalable. A wealth of numerical evaluations using our testbed's measurements allow us to build a solution strategy comprising these methods. Moreover, we show that this strategy finds a near-optimal solution, and achieves a shorter makespan than the baseline scheme by up to 52.3%.Comment: IEEE INFOCOM 202

Traffic Scheduling in Point-to-Multipoint OFDMA-based Systems

The new generation of wireless networks (e.g., WiMAX, LTE-Advanced, Cognitive Radio) support many high resource-consuming services (e.g., VoIP, video conference, multiplayer interactive gaming, multimedia streaming, digital video broadcasting, mobile commerce). The main problem of such networks is that the bandwidth is limited, besides to be subject to fading process, and shared among multiple users. Therefore, a combination of sophisticated transmission techniques (e.g., OFDMA) and proper packet scheduling algorithms is necessary, in order to provide applications with suitable quality of service. This Thesis addresses the problem of traffic scheduling in Point-to-Multipoint OFDMA-based systems. We formally prove that in such systems, even a simple scheduling problem of a Service Class at a time, is NP-complete, therefore, computationally intractable. An optimal solution is unfeasible in term of time, thus, fast and simple scheduling heuristics are needed. First, we address the Best Effort traffic scheduling issue, in a system adopting variable-length Frames, with the objective of producing a legal schedule (i.e., the one meeting all system constraints) of minimum length. Besides, we present fast and simple heuristics, which generate suboptimal solutions, and evaluate their performance in the average case, as in the worst one. Then, we investigate the scheduling of Real Time traffic, with the objective of meeting as many deadlines as possible, or equivalently, minimizing the packet drop ratio. Specifically, we propose two scheduling heuristics, which apply two different resource allocation mechanisms, and evaluate their average-case performance by means of a simulation experiment

Optimal resource allocation In base stations for mobile wireless communications

Telecommunications provides a rich source of interesting and often challenging optimisation problems. This thesis is concerned with a series of mixed-integer non-linear optimisation problems that arise in mobile wireless communications systems. The problems under consideration arise when mobile base stations have an Orthogonal Frequency-Division Multiple Access (OFDMA) architecture, where there are subcarriers for data transmission and users with various transmission demands. In such systems, we simultaneously allocate subcarriers to users and power to subcarriers, subject to various constraints including certain quality of service (QoS) constraints called rate constraints. These problems can be modelled as Mixed Integer Non-linear Programmes (MINLP). When we began the dissertation, we had the following main aims: To design an exact algorithm for the subcarrier and power allocation problem with rate constraints (SPARC), the objective of which is to maximise total data transmission rate of the entire system. To design an exact algorithm for the fractional subcarrier and power allocation problem with rate constraints (F-SPARC) problem in order to maximise system efficiency, i.e.: total data transmission rate divided by total power supplied to the system. To design a heuristic algorithm for the F-SPARC problem. To design a heuristic algorithm for the SPARC problem in dynamic settings, where user demand changes very frequently. Along the way, however, we discovered a new approach to a broad family of problems, which includes the F-SPARC as a special case. These problems are called mixed-integer fractional programs with indicator variables, and they are dealt with in Chapter 3