WLC38-2: Proportional Fairness in Multi-channel Multi-rate Wireless Networks

Subcarrier assignment considering path loss for multiuser OFDM systems with proportional fairness

This paper studies how to assign radio interfaces to work on multiple channels in multi-channel and multi-interface wireless networks. Different from previous studies that focused on the MAC layer, we study this problem from a global perspective. We use a layered graph to model multi-channel wireless networks, and develop effective interface assignment and routing path computation algorithms based on the layered graph. We have evaluated the performance of our algorithm and compared it to a sequential interface assignment algorithm. The numerical results show that our algorithm significantly outperforms the sequential interface assignment.

As wireless networks are gaining increasing popularity, the network energy efficiency has become a critical issue. In this paper, we focus on energy-efficient networking in a generic multi-radio multi-channel (MR-MC) wireless network where transmission scheduling, transmit power control, radio and channel assignment are coupled together in a multi-dimensional resource space, thus requiring joint optimization and low complexity algorithms. We propose a novel Decomposed Approach For energy-efficient (DAFEE) networking in MR-MC networks, with the objective to minimize network energy consumption while guaranteeing a certain level of performance. In particular, we leverage a multi-dimensional tuple-link based model and a concept of resource allocation pattern to transform the complex optimization problem into a linear programming (LP) problem. The LP problem however has a very large solution space due to the exponentially many possible resource allocation patterns. We then exploit delay column generation and distributed learning techniques to decompose the problem and solve it with an iterative process. Furthermore, we propose a sub-optimal algorithm to speed up the iteration with constant-bounded performance. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Designing QoS-aware medium access control (MAC) scheme is a challenging issue in vehicular ad hoc networks. Proportional fairness and bandwidth utilization are among the significant requirements that should be taken into account by a MAC scheme. In this paper, a bandwidth-efficient and fair multichannel MAC protocol is proposed to address these two requirements, specifically in vehicle-to-vehicle communications. The proposed scheme is based on clustering of vehicles and exploits time division multiple access (TDMA) method alongside the carrier sense multiple access with collision avoidance mechanism to allocate DSRC-based resources in a different manner from IEEE 802.11p/IEEE 1609.4 protocols. It divides each channel into aligned dynamic-sized time frames. In each time frame, in a fully TDMA-based period, transmission opportunities are assigned to vehicles letting them have dedicated transmissions on the service and control channels. The maximum number of transmission opportunities per each frame is determined by the cluster head (CH) based on a defined optimization problem which aims at maximizing both proportional fairness and bandwidth utilization. Furthermore, the bandwidth utilization is assumed to be enhanced more through reallocation of unused transmission opportunities in each time frame, using a proposed reallocation algorithm. The proposed MAC protocol is treated as a lightweight scheme such that various types of unicast, multicast and broadcast communications are possible within the cluster without involving the CH. Evaluation results show that the proposed scheme has more than 90 % achievement in terms of proportional fairness and bandwidth utilization simultaneously, and in this case, has a considerable superiority over TC-MAC. In addition, using the proposed scheme, the satisfaction level of vehicles is preserved appropriately.

In this paper, we study the problem of resource allocation in multiradio multichannel (MRMC) networks. Specifically, under the consideration of diverse quality-of-service (QoS) requirements, we propose a fair QoS-aware resource-allocation scheme for MRMC networks. In this scheme, channel allocation, multipath routing, link scheduling, and radio assignment are jointly considered. The proposed scheme is obtained by formulating the resource-allocation problem into a cross-layer network utility maximization (NUM) problem, in which resources are allocated to services with proportional fairness. To avoid oscillation among optimal solutions due to the use of multipath routing, we transform the optimization program into an equivalent program by utilizing the proximal optimization algorithm. Then, we obtain the optimal solution of the latter by the decomposition method. Numerical results show that the proposed scheme indeed achieves proportional fairness according to diverse QoS requirements while maximizing utility.

We have developed a novel downlink packet scheduling scheme for a multiuser OFDMA system in which a subchannel can be time-multiplexed among multiple users. This scheme which is called Matrixed-based Proportional Fairness can provide a high system throughput while ensuring fairness. The scheme is based on a Proportional Fairness (PF) utility function and can be applied to any of the PF-based schedulers. Our scheduler explores multichannel multiuser diversity by using a two-dimensional matrix combining user selection, subchannel assignment, and time slot allocation. Furthermore, unlike other PF-based schemes, our scheme considers finitely backlogged queues during the time slot allocation. By doing so, it can exploit multichannel multiuser diversity to utilize bandwidth efficiently and with throughput fairness. Additionally, fairness in the time domain is enhanced by limiting the number of allocated time slots. Intensive simulations considering finitely backlogged queues and user mobility prove the scheme's effectiveness.

This paper studies the performance of proportional fair space time scheduling in the presence of imperfect channel state information (CSI). We consider the uplink scheduling in the multiuser single-input-multiple-output (SIMO) system. There are K mobiles (with single antenna) and a base station (with n/sub R/ receive antennas) in the system. The space time scheduling problem is formulated with proportional fair (PF) utility function and a novel heuristic solution is presented. We found that in general, with perfect CSI, the system performance improves as n/sub R/ increases due to the extra spatial dimensions. However, at moderate CSI errors, the system performance saturates with respect to SNR and n/sub R/. This is due to the misscheduling and packet outage problem. To address the poor performance in the presence of CSI error, we proposed a low-complexity enhancement, namely proportional rate discounting, to enhance the scheduler's robustness in the presence of CSI error.

COPE, presented by Katti et al. (Proc. ACM SIGCOMM 2006, pp.243-254), exploits the broadcast nature of the wireless medium through opportunistic network coding. In this paper, we introduce the basic knowledge and characteristic of COPE, and propose a multi-channel multi-interface wireless network coding algorithm based on guesswork (GUESS-MCMI-COPE). By reducing the space of reception reports and implementing multi-channel multi-interface, GUESS-MCMI-COPE can increase network throughput. We evaluate our design on NS2, and discuss the results. The results show that the performance of GUESS-MCMI-COPE is superior to that of COPE.

In this letter, an Opportunistic Interference Cancellation (OIC) is first introduced as a rate control strategy for secondary user in cognitive wireless networks. Based on the OIC rate control method, an optimal power control strategy for multichannel cognitive wireless networks is proposed. The algorithm aims to maximize the total transmit rate of cognitive user through appropriately controlling the transmit power of each subchannel under the constraint that the interference temperature at the primary receiver is below a certain threshold. Three suboptimal power control methods, namely Equal Power Transmission (EPT), Equal Rate Transmission (ERT) and Equal Interference Transmission (EIT), are also proposed. The performances of the proposed power control methods are compared through numerical simulations.

This work studies the power optimal dynamic scheduling problem in multi-channel multi-user wireless access networks. Users have quality-of-service (QoS) requirements on the minimum rates with statistical delay guarantees. Only one user is allowed to transmit over a channel in a given time slot. This work considers two scenarios: homogeneous and heterogeneous users. For the former scenario, the optimal scheduling policy can be derived, and an online scheduling algorithm for the optimal policy is proposed using online time-averaging without requiring a-priori known fading statistics. For the latter scenario, the optimal scheduling problem is combinatorially hard; hence, even when the fading statistics are available, computing the optimal policy is intractable. Consequently, this work develops a sub-optimal online scheduling algorithm with linear complexity which does not require a-priori known fading statistics. Moreover, the scheduling algorithm satisfies the QoS constraints for the users. Illustrative results demonstrate the performance of the proposed scheduling algorithms in various settings.

In this paper, we develop an energy-efficient resource-allocation scheme with proportional fairness for downlink multiuser orthogonal frequency-division multiplexing (OFDM) systems with distributed antennas. Our aim is to maximize energy efficiency (EE) under the constraints of the overall transmit power of each remote access unit (RAU), proportional fairness data rates, and bit error rates (BERs). Because of the nonconvex nature of the optimization problem, obtaining the optimal solution is extremely computationally complex. Therefore, we develop a low-complexity suboptimal algorithm, which separates subcarrier allocation and power allocation. For the low-complexity algorithm, we first allocate subcarriers by assuming equal power distribution. Then, by exploiting the properties of fractional programming, we transform the nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form, which includes a tractable solution. Next, an optimal energy-efficient power-allocation algorithm is developed to maximize EE while maintaining proportional fairness. Through computer simulation, we demonstrate the effectiveness of the proposed low-complexity algorithm and illustrate the fundamental trade off between energy and spectral-efficient transmission designs.

Power efficient ultra-wideband based wireless communication for wireless body area network applications

In this paper, a concept of utility proportional fairness is defined, which achieves fairness of utility for different applications. The optimization problem of utility proportional fairness has been formulated meanwhile the optimal solution is presented. The utility proportional fairness is applied in the orthogonal frequency division multiplexing (OFDM) wireless networks, and the optimal subcarrier allocation for utility proportional fairness is deduced. An effective and practical simplified subcarrier allocation algorithm is proposed, in which the instantaneous data rate is substituted by average rate via exponentially weighted low pass time window. Simulation results illustrate that both the optimal and simplified subcarrier allocation algorithms guarantee the utility fairness between different applications well, and the simplified algorithm achieves a perfect tradeoff between utility fairness and system throughput and provides the quality of server (QoS) for multimedia applications.

IEEE 802.11g is an emerging standard for a high data rate physical (PHY) layer supporting up to 54 Mbps at 2.4 GHz for wireless local area networks (WLANs). IEEE 802.11g is defined to be a superset of the 802.11b PHY. It includes the 802.11b modulation schemes and other higher rate modulation schemes, such as the orthogonal frequency division multiplexing (OFDM) schemes originally defined for 802.11a PHY at 5 GHz. One major problem with the 802.11g is that the OFDM signal is not detected by many legacy 802.11b devices operating at 2.4 GHz, and hence it introduces a severe problem since the 802.11 medium access control (MAC) is based on carrier-sensing. The 802.11g draft specification recommends the exchange of request-to-send (RTS)/clear-to-send (CTS) frames modulated with an 802.11b scheme before the transmission of the actual OFDM data frame. However, the RTS/CTS exchanges involve a large bandwidth waste. We propose an alternative solution, which improves the system throughput by avoiding the costly RTS/CTS usage. Our proposed solution utilizes the characteristics of the existing MAC with a minimal rule change, which requires virtually no change in the existing MAC implementation.

SUMMARYIn this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.