Dynamic resource allocation for CDMA‐TDD indoor wireless systems

In this paper, we consider simultaneous wireless information and energy transfer in an orthogonal-frequency-division-multiple-access decode-and-forward relay network, in which an energy-constrained relay node harvests energy from a source node and uses the harvested energy to forward information to multiple destination nodes. Our objective is to maximize the end-to-end sum rate by resource allocation, subject to transmit power constraint at the source and energy-harvesting (EH) constraint at the relay. A non-convex and mixed-integer programming (MIP) problem is formulated to optimize time-switching (TS) ratios of EH and information decoding at the relay, TS ratio of information transmission from relay to destinations, subcarrier allocation as well as power allocation (PA) over all subcarriers at source and relay. We propose to decouple this problem into a convex problem and an MIP problem in fractional form. To solve the MIP problem, we transform it into an equivalent optimization problem in subtractive form which has a tractable solution. As a result, we propose a novel scheme to achieve jointly optimal TS ratios, subcarrier allocation and PA. Simulation results verify the optimality of our proposed resource allocation scheme.

An Application of Interior Point Method to 0-1 Mixed Integer Programing Problems

Real-time power optimization for application server clusters based on Mixed-Integer Programming

The QoS based Resource Allocation Model (Q-RAM) proposed by R. Rajkumar et al. (1998) presented an analytical approach for satisfying multiple quality of service dimensions in a resource constrained environment. Using this model, available system resources can be apportioned across multiple applications such that the net utility that accrues to the end users of those applications is maximized. We present several practical solutions to allocation problems that were beyond the limited scope of Q-RAM. We show that the Q-RAM problem of finding the optimal resource allocation to satisfy multiple QoS dimensions is NP hard. We then present a polynomial solution for this resource allocation problem which yields a solution within a provably fixed and short distance from the optimal allocation. Secondly, Q-RAM dealt mainly with the problem of apportioning a single resource to satisfy multiple QoS dimensions. We study the converse problem of apportioning multiple resources to satisfy a single QoS dimension. In practice, this problem becomes complicated, since a single QoS dimension perceived by the user can be satisfied using different combinations of available resources. We show that this problem can be formulated as a mixed integer programming problem that can be solved efficiently to yield an optimal resource allocation. We also present the run times of these optimizations to illustrate how these solutions can be applied in practice. A good understanding of these solutions will yield insights into the general problem of apportioning multiple resources to satisfy simultaneously multiple QoS dimensions of multiple concurrent applications.

In cellular systems, deploying a large number of antennas at the base station (BS), also called massive multiple-input multiple-output (MIMO), can offer a huge improvement in system throughput. This performance gain achieved through coherent beamforming highly depends on having accurate channel state information (CSI) at the BS. Initially, massive MIMO was considered promising only for time division duplexing (TDD) systems because the downlink training overhead in frequency division duplexing (FDD) systems could be large. To reduce the overhead in FDD systems, we propose efficient eigenspace training and precoding. In the downlink training, the prebeamforming matrix is designed based on the spatial fading correlation to probe the channel and minimize the mean squared error (MSE) in channel estimation. In data transmission, the precoding matrix is designed based on instantaneous CSI to manage the inter-user interference. Our algorithms achieve significant savings in the downlink training because the overhead is associated with the dimension of prebeamforming matrix and no longer limited by the number of BS antennas. Compared with joint spatial division and multiplexing, our algorithms offer much lower MSE and, under some conditions, higher sum rates.

An optimization procedure is described that can serve to identify attractive regionalization plans for large wastewater treatment and collection systems. Potential expansion of facilities in Western Suffolk County, Long Island, New York is considered. A mixed integer programming approach is utilized. Each system element cost, taken as a function of wastewater flow, is represented by a linear variable cost plus a fixed cost. The objective is to minimize total cost while ensuring that all wastewater flow generated is treated. Political and administrative feasibility and the location of effluent discharge constrain the model. The Long Island problem is too large to be solved with a standard formulation of wastewater network as a mixed integer programming problem. To improve computational efficiency, additional constraints are introduced on the minimum flow in each pipe and on the number of pipes leaving each node. Network configurations obtained for Western Suffolk County show that limiting development plans to 201 regions is not cost effective. Regional solutions indicate a general trade‐off between lower costs for coastal discharge and increased groundwater recharge.

Benders’ partitioning method is a classical method for solving mixed integer programming problems. The basic idea is to partition the problem by dividing the variables into complicating variables, normally the variables that are constrained to be integer valued, and easy variables, normally the continuous variables. By fixing the complicating variables, a convex sub-problem is generated. Solving this convex sub-problem and its dual generates cutting planes that are used to create a master problem, which when solved generates new values for the complicating variables, which have a potential to give a better solution. In this work, we assume that the optimal solution is given, and we present a way in which the partitioning idea can be used to generate a valid inequality that supports the optimal solution. By adding some of the continuous variables to the complicating variables, we generate a valid inequality, which is a supporting hyperplane to the convex hull of the mixed integer program. The relaxed original programming problem with this supporting valid inequality added will produce interpretable prices for the original mixed integer programming problem. The method developed can be used to generate economically interpretable prices for markets with non-convexities. This is an important issue in many of the deregulated electricity markets in which the non-convexities comes from large start up costs or block bids. The generated prices are, in the case when the sub-problem generated by fixing the integer variables to their optimal values has the integrality property, also supported by nonlinear price functions that are the basis for integer programming duality.

With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.

In this paper, we investigate the joint optimization of power allocation and subcarrier assignment in the OFDM bidirectional systems where two users exchange data assisted by multiple decode-and-forward (DF) relays. We assume the availability of imperfect channel state information (CSI) that contains a probabilistic uncertainty component and consider its worst effect when performing resource allocation. Moreover, we adopt dynamic network coding to exploit channel diversity. The objective of the resource allocation problem is to maximize the weighted sum rates of the two users subject to quality-of-service (QoS) requirements and individual power constraints. We formulate the problem as a mixed integer programming problem and solve it efficiently in an asymptotic manner by using the dual method. Simulation results show that the proposed scheme is robust to imperfect CSI and provides better performance compared to the scheme with static network coding.

Energy efficient resource allocation based on relay selection and subcarrier pairing with channel uncertainty in cognitive radio network

Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency-division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal anymore. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This article proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then, a key generation neural network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this article can be used for resource-constrained IoT devices in FDD systems.

The code division multiple access system with time division duplex mode (CDMA/TDD) where the same frequency allocation is made for both uplink and downlink is a good solution when the traffic asymmetry between uplink and downlink exists. However, when the asymmetry of each cell may be significantly different from each other, the traffic may experience severe interference from each other when each cell's own slot allocation is made according to the level of traffic asymmetry in each cell. The interference in the W-CDMA/TDD system when the slot allocations are made according to traffic patterns of each cell is analyzed, and a time slot allocation scheme considering the traffic asymmetry is proposed. In this paper, system performances are simulated under the proposed time slot allocation strategy and its optimal operation of the strategy is investigated.

Channel reciprocity is limited in frequency division duplex (FDD) system, and thus those channel reciprocity-based secret key extraction methods which are widely adopted in time division duplex (TDD) systems can hardly be implemented in FDD systems. In this paper, a novel secret key extraction method in OFDM-FDD system is proposed to achieve physical-layer security, by which the channel frequency response (CFR) in the uplink at the same time is estimated by the communication pair with the aid of a specially designed forwarding strategy based on confidential probing signal, and then the secret key is extracted from the CFR estimates. The effectiveness of the proposed method is verified by numerical simulations.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

In green transportation systems, Vehicular Edge Computing (VEC) is playing a critical role by enabling real-time responses to compute-intensive tasks. Furthermore, incentivizing diverse stakeholders in VEC to share resources and ensuring equitable resource allocation are pivotal to achieving sustainability. In existing studies, a key limitation is that resource price is often simplified to a static decision factor, thus failing to function as a dynamic indicator that reflects real-time load state of the RSUs and the essential balance between the total utility of RSUs and vehicle user experience (QoE). At the same time, the issue is more challenging when facing the load balancing within multi-RSU. To resolve this issue, the paper builds a differentiated resource price model and designs a pricing strategy that combines vehicle QoE and RSUs cost, in order to maximize the total utility of RSUs. Specifically, the paper propose an Incentive-Driven Collaborative Resource Management (IDCRM) algorithm, which integrates the Soft Actor-Critic (SAC) based Computing Resource Allocation and Pricing (SCRAP) algorithm for dynamic resource allocation and pricing by individual RSU, and RSUs cooperative offloading algorithm to address multi-RSU load balancing. The experimental results demonstrate that our proposed IDCRM effectively maximizes the provider’s revenue while ensuring vehicle QoE and significantly alleviates the resource bottleneck of a single RSU through a collaborative mechanism, achieving efficient load balancing across multi-RSU.