Queue-Aware Priority Based Scheduling and Power Allocation in Full-Duplex OFDMA Cellular Networks

With the promise of doubling a wireless net-work's capacity, researchers on full-duplex wireless networks need to address multiple issues such as user equipment pairing, power control, and the availability of channel state information. In this paper, we propose a discrete simulator for scheduling and power allocation proposals in full-duplex orthogonal frequency division multiple access networks. Our simulator is queue-aware and adaptable to different scheduling objectives. It enables varying the path-loss model, user distribution, noise conditions, and throughput demands. Additionally, it permits the calculation of packet level performance metrics such as the waiting delay.

Multiuser Orthogonal Frequency Division Multiple Access (OFDMA) networks, such as Long Term Evolution networks, use the frequency reuse-1 model to face the tremendous increase of mobile traffic demands, and to increase network capacity. However, inter-cell interference problems are generated, and they have a negative impact on cell-edge users performance. Resource and power allocation should be managed in a manner that alleviates the negative impact of inter-cell interference on system performance. In this paper, we formulate a novel centralized multi-cell resource and power allocation problem for multiuser OFDMA networks. The objective is to maximize system throughput while guaranteeing a proportional fair rate for all the users. We decompose the joint problem into two independent problems: a resource allocation problem and a power allocation problem. We prove that each of these problems is a convex optimization problem, and that their optimal solution is also an optimal solution to the original joint problem. Lagrange duality theory and subgradient projection method are used to solve the centralized power allocation problem. We study the convergence of our centralized approach, and we find out that it reduces inter-cell interference, and increases system throughput and spectral efficiency in comparison with the frequency reuse-1 model, reuse-3 model, fractional frequency reuse, and soft frequency reuse techniques.

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

In this paper, we present a joint game-theoretic approach to perform inter-cell interference coordination in uplink multi-cell orthogonal frequency division multiple access networks. The coordinated user scheduling and power allocation are considered simultaneously. We prove the existence of the joint-strategy Nash equilibrium (NE) in which both the user scheduling and power allocation strategy reach NE. Then, we design a distributed joint-strategy iterative algorithm to perform interference-aware resource allocation where only partial information exchange is involved. Simulation results demonstrate the effectiveness of the proposed algorithm.

SummaryIn this paper, we present a QoS‐driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay‐QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade‐off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay‐QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Heterogeneous network is a novel network architecture proposed in Long-Term-Evolution~(LTE), which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in Multi-cell Multi-association Orthogonal Frequency Division Multiple Access (OFDMA) heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation; 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker (KKT) optimal solution by a low complexity algorithm based on the difference of two convex functions approximation (DCA) method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

With the recent advances in radio modules capable of carrying out full duplex (FD) communication, the spectrum efficiency promised by the concept of Cognitive radio (CR) can be stretched even further by incorporating full duplex transceivers in CRs. This incorporation brings about certain challenges in the resource allocation. In this work we try to address some of the challenges in joint channel and power allocation. We proposed a decentralized scheme which is capable of selecting which channels to be used as half duplex and which to be used as FD between a pair of CRs. Then we decomposed the problem into two sub-problems one dealing with channel assignment and the other dealing with power allocation. In the power allocation we did not assume the signal to interference plus noise ratio to be high like most work in the literature. The monotonic optimization framework was used to solve the power allocation problem. Then the channel assignment problem was approximated by a linear objective function and was solved as a linear integer program.

The advent and advancement of full-duplex (FD) technology in radio transceivers is expected to lead to full-duplex enabled multi-cell (FD-MC) networks in next generation wireless communication systems. Additional interferences between FD nodes and devices are the main challenges for the deployment of such networks. As in an early deployment scenario where the base stations (BS) operate in FD mode while the user equipments (UE) remain in the half-duplex (HD) mode, the self-interference (SI) within the FD-BS, the mutual interference (MI) between the FD-BS's and between the HD-UE's paired for FD scheduling are the major issues that could jeopardize the capitalization of FD system gains if they are not well addressed. In this paper, we present a system architecture for multi-stage cancellation of SI and joint cancellation of MI and residual SI in an FD-MC network. Multiple orthogonal pilots and their derived forms are utilized for channel estimation during a common training period. System-level delay calibration necessary for SI and MI channel estimation and cancellation is also provided.

Small cell, mmWave, and massive Multi-Input Multi-Output (MIMO) technologies in 5G cellular networks becomes inevitable trend caused by killer applications such as holographic video which is traffic-intensive. In this paper, we study a sequential activation beam selection, user scheduling, and power allocation problem in a mmWave network with massive MIMO utilizing a physical layer preceding technique. We aim to maximize the time-averaged utility of users with a time-averaged transmit power constraint on top of the EdgeSON architecture, which takes advantage of both centralized and distributed characteristics. We decompose the original long-term problem into two-time scales in which solving the problem of choosing beam pattern is run at a slower time scale than solving user scheduling and power allocation. Then, to solve user scheduling and power allocation, we leverage the Lyapunov drift-plus-penalty framework which transforms an original long-term average problem into per-slot modified sub-problems. Since provided per-slot problem to find a set of user scheduling and power allocation is known as NP-hard, we propose a low-complex and practical interference management algorithm, namely CRIM, by introducing two critical users with the highest interference channel gain in intra-BS and inter-BS respectively. Finally, via extensive simulations, we verify and compare the performance of the proposed algorithm and comparing algorithms in a real mmWave network environment.

International audience

The resource allocation of Orthogonal Frequency Division Multiple Access (OFDMA) is one of the core issues in the next generation mobile systems. The improvement in the performance and quality of service (QoS) of communication systems is relying upon the efficient utilization of the available communication resources. The resource allocation of the OFDMA systems is mainly depends on both power and subcarrier allocations of each user for different operation scenarios and channel conditions. This paper proposes and applies Firefly Pack Algorithm (FPA) to find the optimal or near optimal power and subcarrier allocations for OFDMA systems. It takes into consideration the power and subcarrier allocations constrains, channel and noise distributions, distance between users equipments and base station, user priority weight to approximate the most of the variables, constrains, and parameters that encounter in the OFDMA systems. Four important cases for the number of subcarriers and users are addressed, simulated, and analyzed with employing the FPA algorithm under specific operation scenarios to meet the standard specifications. The results demonstrate that FPA is an effective algorithm in finding the optimal or near optimal for both subcarrier and power allocation.

In this paper, a joint subcarrier and power allocation algorithm is proposed to improve the physical layer security in cooperative orthogonal frequency division multiple access (OFDMA) networks, where several source-destination pairs and one untrusted relay are involved. The relay is friendly and intends to help these pairs to enhance their communications. However, it may be overheard by a malicious eavesdropper at the same time. To optimize the joint subcarrier and power allocation with low complexity, we divide the optimization problem into two simpler subproblems. Firstly, the subcarriers are assigned to the source-destination pairs by employing the dual approach under the assumption that the relay power is equally allocated to all the subcarriers. Then, the relay power is allocated to the subcarriers based on the alternative ascending clock auction mechanism. In addition, we prove that the two subproblems can converge after a finite number of iterations. We also find that the proposed auction is cheat-proof and, thus, can avoid the cheating behaviors in the auction process. Numerical results demonstrate that our proposed algorithm can effectively improve the system sum secrecy rate, and the convergence performance is also desirable.

In this paper, we formulate an optimization problem for resource allocation and scheduling in orthogonal frequency division multiple access (OFDMA) networks. Our problem formulation takes into account artificial noise generation to combat a passive multiple antenna eavesdropper and the effects of imperfect channel state information at the transmitter (CSIT) in slow fading. The optimization problem is solved by dual decomposition which results in an iterative resource allocation algorithm with a fast speed of convergence. The packet data rate, secrecy data rate, power, and subcarrier allocation policies are optimized to maximize the average secrecy outage capacity (bit/s/Hz securely and successfully delivered to the users). Simulation results illustrate that our proposed iterative algorithm converges to the optimal solution in a small number of iterations and guarantees a non-zero secrecy data rate for given target secrecy outage and channel outage probability requirements.

A channel-aware and queue-aware (CAQA) scheduling scheme is proposed for the downlink of orthogonal frequency division multiple access (OFDMA) networks. The scheduling scheme cooperates with a rate control policy, which controls arrival rates to the base station queues. The network resource allocation is formulated as a utility maximisation problem, which is decomposed into a CAQA OFDMA scheduling problem and a rate control problem. The authors decompose the scheduling problem into subproblems of rate allocation to subcarriers and propose a joint subcarrier assignment and rate allocation (JSARA) solution for the problem. Simulation is conducted to evaluate the performance of JSARA in terms of throughput, queue length and dropping probability. In addition, JSARA performance gain in allocating users’ arrival rates and achieved aggregate utility, in cooperation with a rate control policy, is investigated.