Subchannel Selection Research Articles

Subchannel selection methods for 3GPP C-V2X networks by considering vehicular mobility

Subchannel selection methods for 3GPP C-V2X networks by considering vehicular mobility

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks

The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space–air–ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the ϵ-constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution.

Joint offloading decision and resource allocation in vehicular edge computing networks

With the rapid development of Intelligent Transportation Systems (ITS), many new applications for Intelligent Connected Vehicles (ICVs) have sprung up. In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles, computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention. However, the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges. In this paper, we propose a heterogeneous Vehicular Edge Computing (VEC) architecture with Task Vehicles (TaVs), Service Vehicles (SeVs) and Roadside Units (RSUs), and propose a distributed algorithm, namely PG-MRL, which jointly optimizes offloading decision and resource allocation. In the first stage, the offloading decisions of TaVs are obtained through a potential game. In the second stage, a multi-agent Deep Deterministic Policy Gradient (DDPG), one of deep reinforcement learning algorithms, with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection. The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.

Energy efficiency maximization for multi-carrier cooperative non-orthogonal multiple access systems

Driven by massive connections and exponential growth of data in mobile devices, Non-orthogonal multiple access (NOMA) has become a candidate access technology to meet the requirement of Internet of things (IoT) in the future network. The combination of multi-carrier technology and cooperative communication is an efficient solution to significantly improve the energy efficiency of NOMA systems. Hence, we consider the energy efficiency (EE) maximization problem for the multi-carrier cooperative NOMA (MC-C-NOMA) systems with amplifying-and-forward (AF) protocol. As the EE maximization problem is mixed-integer nonlinear programming (MINLP), it is difficult to design the resource allocation algorithm to find the optimal solution. To make the EE maximization problem easier to handle, we decouple the EE maximization problem into sub-channel selection and power allocation sub-problems. First, under each user's minimum data rate and maximum transmit power constraints, a matching algorithm is proposed to achieve sub-channel allocation, which first matches the user with the sub-channel. Secondly, the Dinkelbach-like algorithm is used to achieve power allocation. Then, a joint sub-channel selection and power allocation algorithm is proposed to maximize the total energy efficiency (EE) of users in the MC-C-NOMA system. Finally, simulation results are presented to show that the proposed algorithm can achieve higher energy efficiency compared with the existing algorithms.

Covert Wireless Communication in Multichannel Systems

In this letter, we propose a random sub-channel selection covert transmission (RSCT) scheme in multi-channel systems to enhance the covertness of communication. We derive the expression of minimum detection error probability (DEP) and the average effective rate, based on which an optimization is constructed to maximize the average effective covertness rate (ECR). Numerical results indicate that, compared with the single-channel scheme, the proposed scheme can indeed enhance the covertness of the communication and the performance is improved as the number of sub-channels increases. Besides, it is demonstrated that there exists an optimal number of sub-channels for maximizing the average ECR.

Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a “best-effort” at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.

PSO-based Resource Allocation in Software-Defined Heterogeneous Cellular Networks

A heterogeneous cellular network (HCN) is useful to increase the spectral and energy efficiency of wireless networks and to reduce the traffic load from the macro cell. The performance of the secondary user equipment (SUE) is affected by interference from the eNodeB (eNB) in a macro cell. To decrease the interference between the macro cell and the small cell, allocating resources properly is essential to an HCN. This study considers the scenario of a software-defined heterogeneous cellular network and performs the resource allocation process. First, we show the system model of HCN and formulate the optimization problem. The optimization problem is a complex process including power and frequency resource allocation, which imposes an extremely high complexity to the HCN. Therefore, a hierarchical resource allocation scheme is proposed, which including subchannel selection and a particle swarm optimization (PSO)-based power allocation algorithm. Simulation results show that the proposed hierarchical scheme is effective in improving the system capacity and energy efficiency.

Energy Efficient Resource Allocation for UAV-Assisted Space-Air-Ground Internet of Remote Things Networks

Internet of remote things (IoRT) networks are regarded as an effective approach for providing services to smart devices, which are often remote and dispersed over in a wide area. Due to the fact that the ground base station deployment is difficult and the power consumption of smart devices is limited in IoRT networks, the hierarchical Space-Air-Ground architecture is very essential for these scenarios. This paper aims to investigate energy efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. In particular, the optimization goal of this paper is to maximize the system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control and UAV relays deployment. The optimization problem is a mix-integer non-linear non-convex programming, which is hard to tackle. Therefore, an iterative algorithm that combines two sub-problems is proposed to solve it. First, given UAV relays deployment position, the optimal sub-channel selection and power control policy are obtained by the Lagrangian dual decomposition method. Next, based on the obtained sub-channel allocation and power control policy, UAV relays deployment is obtained by successive convex approximation (SCA). These two sub-problems are alternatively optimized to obtain the maximum system energy efficiency. Numerical results verify that the proposed algorithm has at least 21.9% gain in system energy efficiency compared to the other benchmark scheme.

QoE-Oriented Rate Control and Resource Allocation for Cognitive M2M Communication in Spectrum-Sharing OFDM Networks

With the development of wireless communication systems, it is particularly essential to maximize the quality of experience (QoE) of machine-to-machine (M2M) communication. In this paper, we propose a new QoE-oriented uplink rate control and resource allocation scheme for the Internet of Things (IoT) network, by introducing an evaluation model based on mean opinion score (MOS) for different machine-type communication (MTC) devices. The existing works are only dedicated to solving the short-term resource allocation problems by considering the current transmission time slots, which cannot handle long-standing problems. To this end, based on the recently developed Lyapunov optimization, we convert the original long-term optimization problem into the admission rate control subproblem and the resource allocation subproblem in each time slot. To solve the joint power optimization and sub-channel selection subproblems, Gale-Shapley algorithm is utilized to formulate it as a two-dimensional matching problem, and the preference lists are established by the transmission rate and signal to interference plus noise ratio (SINR). In the proposed algorithms, a priority mechanism is employed to ensure fairness. The simulation results demonstrate that without prior knowledge of the data arrivals and sub-channel statistics, the proposed algorithms can significantly improve the overall perceived quality from the users' perspective.

A Distributed Approach to Improving Spectral Efficiency in Uplink Device-to-Device-Enabled Cloud Radio Access Networks

Device-to-device (D2D)-enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two users. However, due to the need to acquire global channel state information (CSI) and to execute centralized algorithms, heavy burdens are placed on the fronthaul and the baseband unit (BBU) pool. To alleviate these burdens, a distributed approach to mode selection and resource allocation for potential D2D pairs under pre-determined resource allocation of C-RAN users is proposed, in which pairs of users are endowed with decision-making capabilities. The proposed procedure is divided into three stages: communication mode and subchannel selection, utility value determination, and reinforcement-learning-based strategy update. The core idea is that the D2D pairs self-optimize the mode selection and resource allocation without global CSI under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the impacts of the fronthaul capacity, the centralized signal processing capability of the BBU pool, and the distance between the D2D transmitter and the remote radio head are demonstrated and analyzed.

On Channel Selection for Carrier Aggregation Systems

In this paper, the problem of sub-channel selection for carrier aggregation (CA) systems is examined. CA enables the achievement of high data rate links via simultaneous transmissions over multiple component carriers. A CA system usually occupies only a limited number of sub-channels $M$ of these components due to limitations on the maximum permitted number of sub-channels per system. From an information theoretic point of view, a CA system should detect and employ the $M$ -best sub-channels out of the $N$ available ones. To that end, such a system probes a subset of sub-channels during each coherence time via pilot transmission. Then, for the best $M$ sub-channels, one-bit feedback information is transmitted in order to prohibit the transmission through sub-channels with gain below a threshold. The aim is to derive tractable forms via employing the extreme value theory for the sum rate (lower bound on ergodic capacity) achieved by the system under Rayleigh fading and then, to optimize jointly the training length and power, the number of the probed sub-channels (probing bandwidth size) and the feedback threshold such that the sum rate is maximized by considering the sub-channel estimation error. The accuracy of the theoretical analysis is verified by numerical results.

Capacity of multi-channel cognitive radio systems: bits through channel selections

The channel capacity of multi-channel cognitive radio systems is studied with the assumption of limited sensing capability. The randomness of sub-channel selection is utilized to convey information. Two types of sub-channels, memoryless and finite state, are considered. For both cases, the separation of sub-channel input distribution optimization and sub-channel selection policy optimization is proved. For the memoryless case, explicit expressions for optimal sensing policy are obtained. For the finite state case, the optimization of channel capacity is considered as a Markov decision problem that maximizes average award. By using Markov decision theory, it is shown that, for the finite state case, the channel capacity is determined by the static distribution of state.

Random Subchannel Selection of Store-Carry and Forward Transmissions in Traffic Hotspots

Traditional direct transmission or cooperative relaying can hardly meet the traffic demand in the traffic hotspots of the future wireless networks due to the limited radio resources. The store-carry and forward (SCF) scheme can address this issue to some extent, but still suffers from inter-user interference (IUI) of uncoordinated user-to-user (U2U) communications. Existing radio resource allocation approaches can decrease the IUI, but the additional uplink signaling overhead (USO) consumes too much radio resources. To address this issue, we propose a random subchannel selection-based SCF (SCF-RSS) scheme, where a USO-free user selection protocol is designed to establish U2U links. Simulations show that the proposed SCF-RSS can improve the system throughput and energy efficiency compared with the existing SCF.

Does Gaussian Approximation Work Well for the Long-Length Polar Code Construction?

Gaussian approximation (GA) is widely used to construct polar codes. However, when the code length is long, the subchannel selection inaccuracy due to the calculation error of conventional approximate GA (AGA), which uses a two-segment approximation function, results in a catastrophic performance loss. In this paper, new principles to design the GA approximation functions for polar codes are proposed. First, we introduce the concepts of polarization violation set (PVS) and polarization reversal set (PRS) to explain the essential reasons that the conventional AGA scheme cannot work well for the long-length polar code construction. In fact, these two sets will lead to the rank error of subsequent subchannels, which means that the orders of subchannels are misaligned, which is a severe problem for polar code construction. Second, we propose a new metric, named cumulative-logarithmic error (CLE), to quantitatively evaluate the remainder approximation error of AGA in the logarithm. We derive the upper bound of CLE to simplify its calculation. Finally, guided by PVS, PRS, and CLE bound analysis, we propose new construction rules based on a multi-segment approximation function, which obviously improve the calculation accuracy of AGA so as to ensure the excellent performance of polar codes especially for the long code lengths. Numerical and simulation results indicate that the proposed AGA schemes are critical to constructing high-performance polar codes.

Hierarchical-game-based algorithm for downlink joint subchannel and power allocation in OFDMA femtocell networks

Heterogeneous wireless networks are promising technologies to provide high-data rate services. Our study proposes a hierarchical joint subchannel and power allocation algorithm in orthogonal frequency division multiple access femtocell networks. FBSs and MBSs in the network maximize their capacity and compete with each other through resource allocation strategy. This competition is captured in the framework of a Stackelberg game, and the Stackelberg equilibrium is introduced as the solution to the problem. An iterative method is proposed for MBSs and FBSs to achieve the hierarchical Stackelberg equilibrium. We specifically recommend a new power allocation function for MBS. The best response strategy of MBS can be directly obtained by using the proposed power-allocation function. As a result, costs are decreased. We also divide the strategy of MBSs into two substrategies, namely, subchannel selection strategy and power strategy. The two substrategies interact with each other in the game process to improve the flexibility of the MBS strategy. A dynamic step-size mechanism is proposed to accelerate the algorithm. Simulation results are presented to show the effectiveness of the proposed algorithm under high-interference conditions.

Transmit Power Minimization for MIMO Systems of Exponential Average BER with Fixed Outage Probability

This paper is concerned with a wireless multiple-antenna system operating in multiple-input multiple-output (MIMO) fading channels with channel state information being known at both transmitter and receiver. By spatiotemporal subchannel selection and power control, it aims to minimize the average transmit power (ATP) of the MIMO system while achieving an exponential type of average bit error rate (BER) for each data stream. Under the constraints on each subchannel that individual outage probability and average BER are given, based on a traditional upper bound and a dynamic upper bound of Q function, two closed-form ATP expressions are derived, respectively, which can result in two different power allocation schemes. Numerical results are provided to validate the theoretical analysis, and show that the power allocation scheme with the dynamic upper bound can achieve more power savings than the one with the traditional upper bound.

Online Resource Allocation for Energy Harvesting Downlink Multiuser Systems: Precoding With Modulation, Coding Rate, and Subchannel Selection

This paper proposes an online resource allocation algorithm for weighted sum rate maximization in energy harvesting downlink multiuser multiple-input-multiple-output (MIMO) systems, where the base station transmitter is powered by both a regular energy source and an energy buffer that is connected to an energy harvester. Taking into account the discrete nature of the modulation and coding rates (MCRs) used in practice, we formulate a stochastic dynamic programming (SDP) problem to jointly design the MIMO precoders, select the MCRs, assign the subchannels, and optimize the energy consumption over multiple time slots with causal and statistical energy arrival information and statistical channel state information. Solving this high-dimensional SDP entails several difficulties: the SDP has a nonconcave objective function, the optimization variables are of mixed binary and continuous types, and the number of optimization variables is on the order of thousands. We propose a new method to solve this NP-hard SDP by decomposing the high-dimensional SDP into an equivalent three-layer optimization problem and develop efficient algorithms to solve each layer separately. The decomposition reduces the computational burden and breaks the curse of dimensionality successfully. We analyze the complexity of the proposed algorithm and demonstrate the performance gains based on numerical examples.

Energy-Efficient OFDMA-Based Two-Way Relay

Energy-efficient orthogonal frequency division multiple access (OFDMA) aims to relieve the booming energy consumption in wireless communications networks and has recently received lots of attention. Meanwhile, two-way relay has been extensively studied and proves to be more spectral-efficient than direct transmission and one-way relay in many scenarios. In this paper, we study energy-efficient resource allocation for OFDMA-based two-way relay, maximizing the aggregated energy efficiency (EE) utility while provisioning proportional fairness in EE among different terminal pairs. The energy-efficient joint power and subchannel allocation and active subchannel selection problem is mixed-integer combinatorial and further demonstrates to be nonconvex. To approach the performance limit, we first find an upper-bound solution relying on continuous relaxation and the Lagrange dual method. To reduce the computational complexity, we separate power allocation and subchannel assignment. To this regard, we exploit the hidden concavity and the pseudoconcavity in the subproblems for any fixed subchannel assignment and propose an EE-oriented sequential subchannel assignment policy. Besides, we discover the sufficient condition for early termination of the sequential subchannel assignment without losing the EE optimality. Simulation results demonstrate that the proposed energy-efficient OFDMA-based two-way relay can achieve much larger EE utility while provisioning proportional fairness in EE among different terminal pairs compared to its spectral-efficient counterpart.

Average Transmit Power Gain of MIMO Fading Channels Over SISO AWGN Channels

This paper is concerned with the average transmit power under adaptive transmission for multiple-input multiple-output (MIMO) antenna systems with multi-beams in fading environments when the channel-state information at both the transmitter and the receiver is available. The transmitter is supposed to be capable of spatiotemporal subchannel selection and power control as eigenvalues of channel matrix changes. Our focus is on an eigenvalue-inversion based optimization transmission scheme. With the scheme, we first introduce a concept of average transmit power gain (ATPG) of MIMO fading channels over single-input single-output additive white Gaussian noise channels in order to analyze deeply relationship between the average transmit power, the number of transmit antennas, the number of receive antennas, and fading channel characterizations. Then in Rayleigh fading we derive several insightful ATPG expressions for single-beam and multi-beam transmissions, respectively. Based on these expressions, the potential amounts of transmit power savings are further estimated in several cases when the underlying MIMO system is very large. Moreover, consideration of extending ATPG is also given in the presence of spatial correlation.