Maximum Achievement Rate Allocation Algorithm for Downlink Multi-User OFDMA Systems

In multi-user OFDMA systems, adaptive resource allocation has been identified as one of the key technologies to have more flexibility and higher efficiency. Several adaptive subcarrier allocation algorithms with the objective to maximize spectral efficiency or fairness have been proposed. However, quality of service (QoS) requirement of each user may not be supported. Some algorithms considering user’s QoS requirement have been introduced, but they do not consider the case that every user’s QoS requirement cannot be guaranteed with limited resources. In this paper, we propose a maximum achievement rate allocation (MARA) algorithm as a new adaptive resource allocation algorithm. The proposed MARA algorithm has a goal to improve overall throughput while maximizing achievement rate, i.e., maximize the number of users meeting QoS requirements. In addition, we investigate that MARA is more effective when fractional frequency reuse (FFR) is adopted as a frequency partitioning scheme. Simulation results show that the MARA algorithm improves the achievement rate as well as overall throughput. Moreover, further performance gains are achieved when FFR is adopted.     

A Fast Subcarrier, Bit, and Power Allocation Algorithm for Multiuser OFDM-Based Systems

In this paper, we propose a real-time subcarrier, bit, and power allocation algorithm for orthogonal frequency-division multiplexing-based multiuser communication systems in downlink transmission. Assuming that base stations know the channel gains of all subcarriers of all users, the proposed loading algorithm tries to minimize the required transmit power while satisfying the rate requirement and data error rate constraint of each user. The novel algorithm simultaneously determines subcarrier, bit, and power allocation by enhancing the suboptimal algorithm by Wong while having the same computational complexity. The proposed scheme offers better performance in terms of transmit power than that of Wong , as demonstrated in the simulation results, whereas the performance of the scheme in Wong was close to that of the optimal solution.     

Downlink user selection and resource allocation for semi-elastic flows in an OFDM cell

We are concerned with user selection and resource allocation in wireless networks for semi-elastic applications such as video conferencing. While many packet scheduling algorithms have been proposed for elastic applications, and many user selection algorithms have been proposed for inelastic applications, little is known about optimal user selection and resource allocation for semi-elastic applications in wireless networks. We consider user selection and allocation of downlink transmission power and subcarriers in an orthogonal frequency division multiplexing cellular system. We pose a utility maximization problem, but find that direct solution is computationally intractable. We first propose a method that makes joint decisions about user selection and resource allocation by transforming the utility function into a concave function so that convex optimization techniques can be used, resulting in a complexity polynomial in the number of users with a bounded duality gap. This method can be implemented if the network communicates a shadow price for power to power allocation modules, which in turn communicate shadow prices for rate to individual users. We then propose a method that makes separate decisions about user selection and resource allocation, resulting in a complexity linear in the number of users.     

OFDM-UWB系统基于用户速率的多用户动态资源分配算法

在原有动态资源分配算法基础上,提出了一种基于用户速率需求的动态资源分配算法。该算法在满足用户数据速率需求和服务质量要求(QoS)的前提下,以用户公平性为原则,分步执行子载波和比特分配来降低系统总的发射功率。首先,通过比较不同子载波对用户速率的影响,引入速率影响因子,对子载波进行分配;然后为每个用户子载波分配比特,并根据用户速率需求进行比特调整。为了进一步降低系统的复杂度,提出了一种通过子载波分组来完成子载波比特分配的方法。仿真结果表明,该算法能够降低系统功耗、误码率和系统复杂度。     

Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.     

Optimum Power Allocation for Single-User MIMO and Multi-User MIMO-MAC with Partial CSI

We consider both the single-user and the multi-user power allocation problems in MIMO systems, where the receiver side has the perfect channel state information (CSI), and the transmitter side has partial CSI, which is in the form of covariance feedback. In a single-user MIMO system, we consider an iterative algorithm that solves for the eigenvalues of the optimum transmit covariance matrix that maximizes the rate. The algorithm is based on enforcing the Karush-Kuhn-Tucker (KKT) optimality conditions of the optimization problem at each iteration. We prove that this algorithm converges to the unique global optimum power allocation when initiated at an arbitrary point. We, then, consider the multi-user generalization of the problem, which is to find the eigenvalues of the optimum transmit covariance matrices of all users that maximize the sum rate of the MIMO multiple access channel (MIMO-MAC). For this problem, we propose an algorithm that finds the unique optimum power allocation policies of all users. At a given iteration, the multi-user algorithm updates the power allocation of one user, given the power allocations of the rest of the users, and iterates over all users in a round-robin fashion. Finally, we make several suggestions that significantly improve the convergence rate of the proposed algorithms.     

多用户OFDM系统中一种新颖的自适应功率分配方案

针对多用户正交频分复用(OFDM)系统自适应资源分配的问题,提出了一种新的自适应子载波分配方案。子载波分配中首先通过松弛用户速率比例约束条件确定每个用户的子载波数量,然后对总功率在所有子载波间均等分配的前提下,按照最小比例速率用户优先选择子载波的方式实现子载波的分配;在功率分配中提出了一种基于人工蜂群算法和模拟退火算法(ABC-SA)相结合的新功率分配方案,并且通过ABC-SA算法的全局搜索实现了在所有用户之间的功率寻优,同时利用等功率的分配方式在每个用户下进行子载波间的功率分配,最终实现系统容量的最大化。仿真结果表明,与其他方案相比,所提方案在兼顾用户公平性的同时还能有效地提高系统的吞吐量,进而证明了所提方案的有效性。     

Resource allocation with proportional rate fairness in orthogonal frequency division multiple access relay networks

We address the problem of subchannel and transmission power allocation in orthogonal frequency division multiple access relay networks with an aim to maximize the sum rate and maintain proportional rate fairness among users. Because the formulated problem is a mixed‐integer nonlinear optimization problem with an extremely high computational complexity, we propose a low‐complexity suboptimal algorithm, which is a two‐step separated subchannel and power allocation algorithm. In the first step, subchannels are allocated to each user, whereas in the second step, the optimal power allocation is carried out on the basis of the given subchannel allocation and the nonlinear interval Gauss–Seidel method. Simulation results have demonstrated that the proposed algorithm can achieve a good trade‐off between the efficiency and the fairness compared with two other existing relevant algorithms. In particular, the proposed algorithm can always achieve 100% fairness under various conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Transmit power adaptation for multiuser OFDM systems

In this paper, we develop a transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission. We generally formulate the data rate maximization problem by allowing that a subcarrier could be shared by multiple users. The transmit power adaptation scheme is derived by solving the maximization problem via two steps: subcarrier assignment for users and power allocation for subcarriers. We have found that the data rate of a multiuser OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain for that subcarrier and the transmit power is distributed over the subcarriers by the water-filling policy. In order to reduce the computational complexity in calculating water-filling level in the proposed transmit power adaptation method, we also propose a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers. Results show that the total data rate for the proposed transmit power adaptation methods significantly increases with the number of users owing to the multiuser diversity effects and is greater than that for the conventional frequency-division multiple access (FDMA)-like transmit power adaptation schemes. Furthermore, we have found that the total data rate of the multiuser OFDM system with the proposed transmit power adaptation methods becomes even higher than the capacity of the AWGN channel when the number of users is large enough.     

Outage Minimization and Rate Allocation for the Multiuser Gaussian Interference Channels With Successive Group Decoding

We consider a memoryless Gaussian interference channel (GIC) where $K$ single-antenna users communicate with their respective receivers using Gaussian codebooks. Each receiver employs a successive group decoder with a specified complexity constraint, to decode its designated user. It is aware of the coding schemes employed by all other users and may choose to decode some or all of them only if it deems that doing so will aid the decoding of its desired user. For a GIC with predetermined rates for all transmitters, we obtain the minimum outage probability decoding strategy at each receiver which satisfies the imposed complexity constraint and reveals the optimal subset of interferers that must be decoded along with the desired user. We then consider the rate allocation problem over the GIC under successive group decoding and design a sequential rate allocation algorithm which yields a pareto-optimal rate allocation, and two parallel rate allocation algorithms which yield the symmetric fair rate allocation and the max-min fair rate allocation, respectively. Remarkably, even though the proposed decoding and rate allocation algorithms use “greedy” or myopic subroutines, they achieve globally optimal solutions. Finally, we also propose rate allocation algorithms for a cognitive radio system.      

A Low Complexity Resource Allocation Method for OFDMA System Based on Channel Gain

In orthogonal frequency division with multiple access (OFDMA) systems dynamic radio resource allocation improves overall performance by exploiting the multiuser diversity gains. A key issue in OFDMA is the allocation of the OFDM subcarriers and power among users sharing the channel. This paper proposes a new rate adaptive resource allocation scheme in the OFDMA downlink transmission system. Our proposed algorithm is based on the users’ sensitivity to the subcarrier allocation which means how frequency selective is the channel from the user’s perspective. As a result of frequency selectivity of the channel, different subchannels of the same user experience different levels of fade. However, how different they undergo fading could be measured by difference between maximum and minimum channel gain of that user. Our proposed method is based on difference between maximum channel gain and minimum channel gain for each user and uniform distribution of power among subcarriers. Simulation results show that the proposed algorithm achieves higher capacity over fixed TDMA method, and reported suboptimal methods with acceptable rate proportionality.     

A low-complexity beamforming-based scheduling to downlink OFDMA/SDMA systems with multimedia traffic

In this paper, we propose a low-complexity beamforming-based scheduling scheme utilizing a semi-orthogonal user selection (SUS) algorithm in downlink orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) systems to support multimedia traffic. One of the challenges in the multi-dimensional (space, time, and frequency) radio resource allocation problem for OFDMA/SDMA systems is its high complexity, especially to simultaneously satisfy the quality of services (QoS) requirements for various traffic classes. In the literature, the SUS algorithm is usually applied to the single-class traffic environment, but extending the SUS algorithm to the multimedia environment is not straightforward because of the need to prioritize the real-time (RT) users and the non-real-time (NRT) users. To solve this problem, we propose the concept of urgency value to guarantee the fairness of the NRT as well as the best effort (BE) users while satisfying the delay requirement for the RT users. Simulation results show that, when traffic load is greater than 0.5, the proposed scheduling algorithm can improve the fairness performance by more than 100% over the most recently proposed algorithms.     

An efficient data rate maximization algorithm for OFDM based wireless networks

In this paper we present a computationally efficient, suboptimal integer bit allocation algorithm that maximizes the overall data rate in multiuser orthogonal frequency division multiplexing (OFDM) systems implemented in wireless networks. Assuming the complete knowledge of a channel and allowing a subchannel to be simultaneously shared by multiple users we have solved this data rate maximization problem in two steps. The first step provides subchannel assignment to users considering the users’ requests on quality of service (QoS) expressed as the minimum signal-to-noise ratio (SNR) on each subchannel. The second step provides transmit power and bit allocation to subchannels in order to maximize the overall data rate. To reduce computational complexity of the problem we propose a simple method which assigns subchannels to users and distributes power and bits among them. We have analyzed the performance of our proposed algorithm by simulation in a multiuser frequency selective fading environment for various signal-to-noise ratios and various numbers of users in the system. We have concluded that our algorithm, unlike other similar algorithms, is suitable for OFDM wireless networks, especially when signal-to-noise ratio in the channel is low. Also, the results have shown that the total data rate grows with the number of users in the system.     

User scheduling and power allocation for nonfull buffer traffic in NOMA downlink systems

Nonorthogonal multiple access (NOMA) is one of the key technologies for 5G, where the system capacity can be increased by allowing simultaneous transmission of multiple users at the same radio resource. The most of the proportional fairness (PF)–based resource allocation studies for NOMA systems assumes full buffer traffic model, while the traffic in real‐life scenarios is generally nonfull buffer. In this paper, we propose User Demand–Based Proportional Fairness (UDB‐PF) and Proportional User Satisfaction Fairness (PUSF) algorithms for user scheduling and power allocation in NOMA downlink systems when traffic demands of the users are limited and time‐varying. UDB‐PF extends the PF‐based scheduling by allocating optimum power levels towards satisfying the traffic demand constraints of user pair in each resource block. The objective of PUSF is to maximize the network‐wide user satisfaction by allocating sufficient frequency and power resources according to traffic demands of the users. In both cases, user groups are selected first to simultaneously transmit their signals at the same frequency resource, while the optimal transmission power level is assigned to each user to optimize the underlying objective function. In addition, the genetic algorithm (GA) approach is employed for user group selection to reduce the computational complexity. When the user traffic rate requirements change rapidly over time, UDB‐PF yields better sum rate (throughput) while PUSF provides better network‐wide user satisfaction results compared with the PF‐based user scheduling. We also observed that the GA‐based user group selection significantly reduced the computational load while achieving the comparable results of the exhaustive search.     

New adaptive bit allocation algorithms for multiuser OFDM/CDMA systems

An adaptive bit allocation algorithm is proposed for multiuser transmission in OFDM/CDMA systems. The proposed scheme takes advantage of frequency diversity to dynamically allocate a suitable number of bits/per symbol on subcarriers of each user based on the transmitting objectives such as the required transmission rate and BER. A suboptimal solution to the problem of the bit allocation on subcarriers for each user is derived by minimizing the interference power from each user. Then an algorithm for adjusting the number of allocated bits is used to further reduce the interference without changing the total transmitted data rate. The performance obtained by minimizing the interference resulting from each user is studied in terms of BER, transmission data rate and the system capacity supporting multiple users. The theoretical analysis and simulation results show that the proposed algorithm substantially outperforms those reported previously.     

Multiuser OFDM with adaptive subcarrier, bit, and power allocation

Multiuser orthogonal frequency division multiplexing (OFDM) with adaptive multiuser subcarrier allocation and adaptive modulation is considered. Assuming knowledge of the instantaneous channel gains for all users, we propose a multiuser OFDM subcarrier, bit, and power allocation algorithm to minimize the total transmit power. This is done by assigning each user a set of subcarriers and by determining the number of bits and the transmit power level for each subcarrier. We obtain the performance of our proposed algorithm in a multiuser frequency selective fading environment for various time delay spread values and various numbers of users. The results show that our proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-division multiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes. We have also quantified the improvement in terms of the overall required transmit power, the bit-error rate (BER), or the area of coverage for a given outage probability     

Resource Allocation for Decode-and-Forward Relaying Assisted Multi-cell Orthogonal Frequency Division Multiplexing Systems with Frequency Planning

In this paper we apply frequency planning to the resource allocation of multi-cell and multi-user relay enhanced orthogonal frequency division multiplexing systems and propose a low-complexity algorithm taking into account of interference coordination, subcarrier and power allocation. We divide each cell into three sectors and allow different subcarrier set that can be used by the users of one sector. Such a method can help to increase the distance among users that use the same subcarriers of adjacent cells, which can reduce the impact of co-channel interference to a certain extent. Therefore, the original problem can be decoupled into three independent sub-problems by means of frequency dividing and adaptive power allocation at base station nodes which can reduce the computing complexity greatly. In the process of resource allocation for single sector, the relationship of transmission power between base station and relay node is used to transform the max-min problem into standard closed expression. With the help of dual decomposition approach, water-filling theorem and iterative power allocation algorithm, the suboptimal solution of the primal problem can be achieved finally. Simulation results illustrate that our proposed algorithm achieves almost the same performance as the optimal resource allocation and reduce the computing complexity greatly. In addition, the proposed algorithm can ensure the users fairness of different sectors.     

Joint Spectrum Allocation and Relay Selection in Cellular Cognitive Radio Networks

Cognitive Radio Network (CRN) has been proposed in recent years to solve the spectrum scarcity problem by exploiting the existence of spectrum holes. One of the important issues in the cellular CRNs is how to efficiently allocate primary user (PU) spectrum inside a CRN cell without causing harmful interference to PUs. In this paper, we present a cross-layer framework which jointly considers spectrum allocation and relay selection with the objective of maximizing the minimum traffic demand of secondary users (SUs) in a CRN cell. Specifically, we consider (1) CRN tries to utilize PU spectrum even when the CRN cell is not completely outside the protection region of the PU cell, and (2) cooperative relay is used in cellular CRNs to improve the utilization of PU spectrum. We formulate this cross-layer design problem as a Mixed Integer Linear Programming (MILP) and propose a low complexity heuristic algorithm to solve it. Compared to a simple channel allocation scheme, the numerical results show a significant improvement by using our proposed method and the performance is close to the optimal solution. We further consider the spectrum allocation among several CRN cells with the objective of maximizing the overall minimum throughput of all cells while ensuring each individual cell’s minimum throughput requirement. A low complexity algorithm is proposed to achieve the objective with satisfactory performance.     

下行多用户CoMP系统资源分配算法

针对下行多用户多点协作传输系统(MU-CoMP)边值自适应(MA)的问题,提出了一种快速资源分配算法。该算法首先根据用户的速率要求及平均信道增益估计出每个用户需要的子载波数目,在此基础上设计合理的子载波分配算法进行子载波分配,通过预判并且剔除不适合传输数据的较差空间子信道实现快速比特加载。仿真结果表明,所提算法在满足用户最低速率和误码率要求的前提下有效地降低了总发射功率,以较低的复杂度获得了接近最优算法的性能。