Ergodic capacity region for the uplink OFDMA network

Energy efficiency (EE) is one fundamental performance metric for design of orthogonal frequency division multiple access (OFDMA) networks besides spectral efficiency (SE). The conventional resource allocation is to maximize data throughput without consideration of EE. In this paper, we propose a new low-complexity resource allocation algorithm to maximize EE, which first meet the quality-of-services (QoS) demand of every user and then assign the rest of subcarriers with criterion of energy efficiency. We also compare this new algorithm with the near-optimal EE algorithm, maximum-power-decrease-first (MPDF), which is proposed in [9]. The proposed algorithm differs from the MPDF algorithm in terms of the number of waterfilling for subcarrier allocation. The waterfilling operation in our proposed algorithm is not related to the number of users so that it reduces the computational complexity significantly. Finally, the simulation results show that the proposed algorithm approach the EE performance of the MPDF algorithm with lower complexity.

Efficient resource allocation for delay-sensitive traffic, such as telephony and video streaming, in Orthogonal Frequency Division Multiple Access (OFDMA) networks is needed to increase system performance. In our system, users try to achieve a low queuing delay and buffer space usage by competing for transmission over the subchannels. We formulate this problem as a bargaining game and use the Nash Bargaining Solution (NBS) to realize a fair and efficient subchannel allocation for the users. Simulation results show performance improvements, with regard to packet dropping and delay distribution, over other algorithms.

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

OFDM (Orthogonal Frequency Division Multiplexing) is a well-known multicarrier modulation technique that allows high-rate data transmissions over multipath broadband wireless channels. By usingOFDM, a high-rate data stream is split into a number of lower-rate streams that are simultaneously transmitted on different orthogonal subcarriers. Thus, the broadband channel is decomposed into a set of parallel frequency-flat subchannels; each one corresponding to an OFDM subcarrier. In a single user scenario, if the channel state is known at the transmitter, the system performance can be enhanced by adapting the power and data rates over each subcarrier. For example, the transmitter can allocate more transmit power and higher data rates to the subcarriers with better channels. By doing this, the total throughput can be significantly increased. In a multiuser scenario, different subcarriers can be allocated to different users, which constitutes an orthogonal multiple access method known as OFDMA (Orthogonal Frequency Division Multiple Access). OFDMA is one of the principal multiple access schemes for broadband wireless multiuser systems. It has being proposed for use in several broadband multiuser wireless standards like IEEE 802.20 (MBWA: http://grouper.ieee.org/groups/802/20/), IEEE 802.16 (WiMAX: http://www.ieee802.org/16/, 2011) and 3GPP-LTE (http://www.3gpp.org/). This chapter focuses on the OFDMA broadcast channel (also known as downlink channel), since this is typically where high data rates and reliability is needed in broadband wireless multiuser systems. In OFDMA downlink transmission, each subchannel is assigned to one user at most, allowing simultaneous orthogonal transmission to several users. Once a subchannel is assigned to a user, the transmitter allocates a fraction of the total available power as well as a modulation and coding (data rate). If the channel state is known at the transmitter, the system performance can be significantly enhanced by allocating the available resources (subchannels, transmit power and data rates) intelligently according to the users’ channels. The allocation of these resources determines the quality of service (QoS) provided by the system to each user. Since different users experience different channels, this scheme does not only exploit the frequency diversity of the channel, but also the inherent multiuser diversity of the system. In multiuser transmission schemes, like OFDMA, the information-theoretic system performance is usually characterized by the capacity region. It is defined as the set of rates 6

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 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.

In this paper, we investigate adaptive resource allocation in downlink transmission of orthogonal frequency division multiple access (OFDMA) networks. A block-level resource allocation scheme is developed to maximize the overall throughput of the networks. We focus on application in long term evolution (LTE) systems where all resource blocks (RB's) allocated to the same user must use the same modulation and coding scheme (MCS). The complexity of such optimization problem is usually high. In order to reduce the complexity, we divide the original optimization problem into two suboptimal ones. We first allocate appropriate RB's to users with the best channel quality and then perform power allocation. Finally, a more effective MCS is selected, which not only ensures block-error rate (BLER) performance of RB with the poor channel condition but also makes full use of the RB's with good channel condition. Simulation results show that the proposed resource allocation scheme can improve the overall throughput of the network by 20% compared with the existing scheme when the number of users is 4 and SINR = 10 dB.

Deployment of relay nodes (RNs) in cellular orthogonal frequency division multiple access (OFDMA) systems provides an effective solution to increase high data rate coverage and improve cell throughput. However, a challenging issue is that additional interferences caused by RNs may substantially compromise the performance gain if no measure is taken. In this study, the authors address the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimising the negative effects of interferences introduced. The authors first analyse the possible interference scenarios in multi-cell systems. Based on the insights into their analysis, the authors propose resource allocation with interference coordination (RAIC) scheme for cellular OFDMA systems. RAIC selectively perform one of three resource allocation algorithms according to the offered traffic load in the system, to mitigate interferences and thus enhance system throughput. The authors conduct intensive simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results demonstrate that their proposed RAIC can effectively improve system throughput compared with the resource allocation schemes without appropriate interference coordination.

Orthogonal Frequency Division Multiple Access (OFDMA) based cellular network can support diverse requirement of multimedia traffic. OFDMA method using adaptive modulation and coding technique can increase the system capacity many fold. In order to meet dynamic bandwidth requirement in downlink, an efficient resource allocation method called Two Stage Rate Adaptive (TSRA) algorithm is proposed here. The TSRA employs an adaptive modulation techniques and a Band Width Adaptation (BWA) algorithm to support the dynamic resource requirement of a multimedia traffic. The BWA is invoked dynamically by TSRA to deal with real-time variable bit rate services. It is observe that the simulated average system capacity exceeds 6 b/s per Hz, and approaches analytical/theoretical limit for increasing no of users. The delay performance is observed for existing and new arrival of multimedia traffic. We also simulate the average BER performance of the TSRA algorithm.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

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.

In this study, the authors propose a fine granularity resource allocation algorithm for enhancing overall quality of users’ received video in the orthogonal frequency division multiple access system. Subcarrier's contribution for video transmission is used as the guideline of resource allocation in this algorithm. The subcarrier with the largest contribution is given priority to be allocated on the basis of two‐level paths search in the whole resource allocation tree. Moreover, the authors design the full search implementation scheme and the simplified implementation scheme for the resource allocation algorithm, respectively. The full search implementation scheme can obtain superior performance gain with high complexity. For reducing the complexity, the authors further propose a simplified implementation scheme subject to some minor quality degradation. At last, the authors compare the fine granularity resource allocation algorithm with other existing resource allocation algorithms for video transmission. In the simulation results, it is shown that the proposed algorithm acquires much more superior performance in overall quality of users’ received video.

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 effective inter-cell interference coordination (ICIC) scheme for co-channel interference in downlink orthogonal frequency division multiple access (OFDMA) networks through cognitive sensing performed by subnet nodes in heterogeneous network (Het-Net). The more co-channel interference is detected and avoided, the more power is saved for both macro BS and subnet nodes. The strategy framework of the cognitive critical ratio and the number of subnet nodes is set up for interference management aiming at maximizing net saving power for the whole system. The framework is modeled as multiple objective non-linear programming (MONLP), employing parallel vector evaluated particle swarm optimization algorithm (VEPSO) to obtain the global optimum solution. Utilizing dynamic traffic loads, we could figure out the optimal deployment of low-power BSs. Finally, the practical implementation in Het-Net for long term evolution advanced (LTE-A) is designed. The numerical evaluation and system simulation results demonstrate the applicability and effectiveness of the proposed interference management framework.