Fairness aware resource allocation for downlink MISO-OFDMA systems

In orthogonal frequency division multiple access (OFDMA) systems, efficient resource allocation is very important to improve the performance of the OFDMA system. The general principle of resource allocation in an OFDMA system is to assign each subcarrier to the user with the best channel condition for that subcarrier. However, in delay tolerant traffic, current work did not consider that packets on retransmissions may have different allocation criteria from those on their first transmissions. In this paper, the resource allocation scheme is investigated by considering different criterions to packets on first transmissions and on retransmissions. Under a bit error rate (BER) constraint, the objective is to maximize the throughput of packets on first transmissions, meanwhile decreasing the longest packet delay caused by retransmitted packets and guaranteeing all packets on retransmissions to be sent out. A low-complexity suboptimal algorithm that separates allocation for packets on retransmissions and on first transmissions is proposed. Adaptive modulation scheme is adopted to guarantee the BER constraint, except the modulation for each retransmitted packet remains the same. In order to improve retransmission liability, the retransmitted packets are combined with its failed packets in previous transmission.

A new concept-traffic diversity is proposed for resource allocation of orthogonal frequency division multiple access system. With traffic diversity, the transmitter designates different bit error rate (BER) level for each kind of traffic. The transmission data rate corresponds to the BER level. At the receiver, this separated traffic, which belong to the same user, will be aggregated. Simulation results show that traffic diversity can increase the throughput by 27%.

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.

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

In the conventional subcarrier and power allocation schemes in multi-input multi-output and orthogonal frequency division multiple access (MIMO-OFDMA) systems, only equal fairness among users has been considered, and no scheme for proportional data rate fairness is considered. In this paper, subcarrier and power allocation criterion is proposed to maximize the total throughput under the constraints of total power and proportional data rate fairness among users. In the proposed scheme, subchannel allocation is firstly performed by using singular value decomposition (SVD) of channel matrix under the constraint of users' data throughput requirements, and then adaptive bit and power loading is applied. Simulation results show that total throughput is increased as well as each throughput is proportionally distributed among users in MIMO-OFDMA systems.

In the subcarrier and power allocation schemes in Multi-Input Multi-Output and Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) systems, only equal fairness among users has been considered and no scheme for proportional data rate fairness has been considered. In this paper, a subcarrier, bit and power allocation scheme is proposed to maximize the total throughput under the constraints of total power and proportional data rate fairness among users. In the proposed scheme, joint subchannel allocation and adaptive bit loading is firstly performed by using singular value decomposition (SVD) of channel matrix under the constraint of users' data throughput requirements, and then adaptive power loading is applied. Simulation results show that effective performance of the system has been improved as well as each throughput is proportionally distributed among users in MIMO-OFDMA systems.

Rapid development of wireless services, leads to ubiquitous personal connectivity in the world. The demand for multimedia interactivity is higher in the world which leads to the requirement of high data transmission rate. Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a future wireless service which is used to overcome the existing service problems such as development of subscriber pool and higher throughput per user. Although it overcomes the problems in existing services, resource allocation becomes one of the major issues in the MIMO-OFDM systems. Resource allocation in MIMO-OFDM is the optimization of subcarrier and power allocation for the user. The overall performance of the system can be improved only with the efficient resource allocation approach. The user data rate is increased by efficient allocation of the subcarrier and power allocation for each user at the base station, which is subject to constraints on total power and bit error rate. In this study, the problem of resource allocation in MIMO-OFDM system is tackled using hybrid artificial bee colony optimization algorithm based on a crossover operation along with Poisson-Jensen in equation. The experimental results show that the proposed methodology is better than the existing techniques.

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.

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.

This paper deals with our investigations into improving the performance of orthogonal frequency division multiple access (OFDMA) system with low computational complexity. In the proposed scheme, by taking attribute of a user channel like channel capacity decrease rate, subcarriers are allocated depending on the individual users' priority. This scheme is high reliable technique by ordering the capacity decrease rate and allocating subcarriers to users depending on the proposed user' priority. Also we apply error-based bit allocation scheme according to channel condition. And to reduce the computational complexity and iterations until reaching a final allocation, simplified adaptation based on bit error rate (BER) is suggested and compared with original algorithm. As the channel evaluation parameter, signal to noise ratio (SNR) estimation method is used. Our proposed scheme is simulated in comparison with a conventional resource allocation scheme. It is shown that proposed method demonstrates better results than the simulated schemes.

We propose an adaptive resource allocation algorithm for a multiple-input multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) based downlink cognitive radio network (CRN). The CRN has multiple secondary users (SUs) coexisting with multiple primary users (PUs). The aim is to maximize the total data throughput of SUs while ensuring interference leakage to PUs is below a threshold and quality of services (QoS) for SUs such as data rates and bit error rate (BER) are met. The algorithm optimally allocates radio resources such as subcarrier, spatial beams, powers and bits to each user using an integer linear programming (ILP) framework. The performance of the algorithm has been evaluated using simulation results.

Resource allocation for a downlink orthogonal frequency division multiple access (OFDMA) system ensures that capacity and throughput are maximized. In the literature, either channel coding is not considered or only a fixed code rate is specified. To overcome this problem, an optimized cross-layer resource allocation strategy that distributes the user, bit and power optimally has been proposed for adaptive modulation and coding (AMC) based single-cell downlink OFDMA systems. The proposed strategy, which is referred to as optimized user, bit and power allocation (OUBPA), considers throughput as a new cost function in terms of spectral efficiency and bit-error rate (BER) to allocate the available resources. An optimized approach is presented for solving the throughput maximization problem and simulation results demonstrating that the throughput performance of the OFDMA-AMC system using OUBPA strategy outperforms conventional techniques.

This paper analyzes the use of Random Frequency Hopping to enable self-organizing Orthogonal Frequency Division Multiple Access systems (RFH-OFDMA). While LTE (Long Term Evolution) macrocell resource planning is typically based on centralized planning of orthogonal deterministic frequency hopping patterns, the integration of femtocells located within macrocells introduces either complex planning efforts or uncontrolled interference issues. The results presented in this paper show that random frequency hopping is a new effective way to reduce interference for the integration of femtocells without resource planning into macrocells. An analytical model for the SINR (Signal-to-Interference-and-Noise Ratio) and a simulation model for the BER (Bit Error Rate) are presented for interfering RFH-OFDMA systems. All OFDMA parameters can be freely selected in time and frequency in the model, enabling to dimension systems with minimal interference. Based on the analytical model, a performance evaluation is presented, which uses typical LTE parameters. Compared to the performance of systems without resource planning and random frequency hopping, a significant gain is achieved for self-organizing RFH-OFDMA systems with respect to the SINR and BER.

In uplink orthogonal frequency division multiple access (OFDMA), carrier frequency offsets (CFO) and/or timing offsets (TO) of other users with respect to a desired user can cause significant multiuser interference (MUI). In this paper, we derive analytical bit error rate (BER) expressions that quantify the degradation of BER due to the combined effect of both CFOs as well as TOs; such an analytical BER derivation has not been reported so far for uplink OFDMA in the presence of CFOs and TOs. For the case when the desired user is perfectly frequency/time aligned (and other users have non-zero CFOs/TOs), we obtain an exact closed-form expression for the BER. For the case when the desired user also has non-zero CFO/TO, we obtain an approximate BER expression involving a single integral. Analytical and simulation results are shown to match quite well.

In this paper subcarrier and power allocation to each user at base-station maximizes the user data rates, subject to constraints on total power and bit error rate. First, each subchannel is assigned to the user with best channel-to-noise ratio for the channel, with random power distributed by water filling algorithm. The Gao's (2006) subcarrier allocation algorithm was used to calculate the power for each population in of genetic algorithm model. With the goal of minimize the overall transmit power while ensuring the fulfillment of each user's data rate and bit error rate (BER), the needed allocation is proposed through genetic search. The proposed genetic search helps fast convergence and can handle large allocations of subcarriers to users without performance degradation. The simulation results show that genetic algorithm approach will be used where complex computations are involved and near optimal solution are acceptable for optimum resource allocation