In this paper, we propose two opportunistic polling algorithms for capacity-region-aware medium access control (MAC) in wireless networks with partial channel state information. Based on the probability density function of the unknown channel gain, the proposed algorithms opportunistically exploit the capacity region of a multiple access channel in network information theory and benefit from successive interference cancellation techniques. Simulation results show that both algorithms could increase the network throughput. Furthermore, the active opportunistic polling algorithm could increase the network throughput by more than 30%.

A group of multi-mode wearable devices or mobile terminals (MTs) may be carried by the same vehicle and then perform group handover during the movement within heterogeneous networks. However, it is a challenging work to determine a proper target network which is good for both MTs and the network provider. This paper presents a simulated-annealing-based group handover decision scheme to reduce the group handover blocking ratio, decrease the number of handover, balance the traffic load between networks, and ensure the target quality of service (QoS) requirements of MTs. Simulation results show that the proposed scheme can reduce the group handover blocking ratio efficiently and balance the load. Besides, when the number of group MTs is not quite large, the number of handover can be reduced and the QoS requirements can be fulfilled.

To meet the soaring demand of high data rate from today's applications of mobile Internet, small cells would be widely deployed to achieve higher data rate and better spectral efficiency in 4G and B4G/5G cellular networks. However, when the network traffic load is low, a large number of small-cell base stations could incur a tremendous energy waste. To overcome this pitfall, in this paper, we propose a traffic-aware power saving scheme (TAPS) by which a small-cell base station can adequately switch its operation mode - namely, normal, sniff and low-duty modes - according to its traffic load to reduce its power consumption. Simulation results show that TAPS can save a large amount of energy but only slightly hamper system performance compared with the case without power-saving scheme.

In this paper, we propose using group partition and dynamic rate adaptation for scalable throughput optimization of capacity-region-aware device-to-device communications. We adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. Based on graph theory, devices are first partitioned into subgroups. To optimize the throughput of a subgroup, instead of directly solving an integer-linear programming problem, we propose using a fast iterative algorithm to select active devices and using aggression levels for rate adaptation based on channel state information. Simulation results show that the proposed algorithm is scalable and could significantly outperform the greedy algorithm by more than 50%.

We propose a novel bargaining-game-based access network selection scheme for call requests in heterogeneous networks (HetNet). The bargaining game takes into consideration a number of factors including the mobility pattern of the mobile device, the load of the candidate access network, and the preference of the candidate access network to the call request. The studied bargaining game has a unique solution since the set of feasible payoff vectors is convex. Simulation results show that the proposed scheme could significantly outperform the TOPSIS scheme and the evolutionary game approach in terms of the handoff occurrence ratio and the call dropping probability.

In this paper, we propose a coordination based resource management (CBRM) scheme with quality-of-service (QoS) provisioning for multicell OFDMA downlink systems. The proposed CBRM scheme cooperates with the multiple point deployment and the enhanced fractional frequency reuse (FFR). To solve the intra-cell interference brought by multiple point deployment, the CBRM scheme adopts a utility function to coordinate TPs and then maximizes system throughput and fulfills QoS requirements. Simulation results show that the CBRM scheme enhances the system throughput by amounts of 113% and 89% compared to the conventional FFR scheme [1] and the dynamic interference avoidance (DIA) scheme [6], respectively. Moreover, the CBRM scheme provides good QoS guarantee for both real-time and non-real-time users.

In this paper, we propose a group partition approach for scalable throughput optimization of capacity-region-aware Device-to-Device communications. Instead of the conventional collision model, we adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. We propose a novel approach to partition devices into subgroups based on coloring a conflict graph and then solve an optimization problem for each subgroup. In particular, the formation of the conflict graph explicitly takes the capacity region into consideration. Simulation results show that the proposed approach could significantly outperform the greedy algorithm.

We propose adaptive capacity-region-aware algorithms for medium access control in wireless networks. In particular, the proposed algorithms are aware of the information-theoretic capacity region of a multiple access channel. According to the proposed algorithms, a node dynamically adjusts its channel access strategy based on the previous strategies of other nodes and the channel feedback. A strategy is composed of a transmission threshold and an aggression level. We propose symmetric learning algorithms for maximizing throughput. In addition, we propose asymmetric learning algorithms to strike a good balance between throughput and fairness. Furthermore, we propose novel methods to properly choose a finite number of available data transmission rates. We use both analytical results and simulation results to justify the usage of the proposed algorithms.

In this paper, we propose a multi-factors cell selection (MFCS) scheme for heterogeneous networks with multimedia traffic. In heterogeneous networks, it is an important issue to determine a suitable access network for call request to support seamless service and high-speed connectivity. The MFCS scheme first chooses candidate cells for call request to satisfy the received signal strength constraint, cell loading constraint, and dwell time constraint. It then determines a target cell from candidate cells based on the QoS factor, loading factor, and mobility factor. Simulation results show that, the MFCS scheme reduces the handoff occurring ratio by 25.5 % and improves the overall system throughput by 15.9 % as compared to the utility and game-theory based network selection (UGNS) scheme [6].

On behalf of the IEEE Communications Society and members of the organizing committee, it is our pleasure to welcome you to the 2013 IEEE 14th International Conference on High Performance Switching and Routing (IEEE HPSR 2013) in Taipei, Taiwan. The conference continues to be a leading forum for practitioners and researchers in this area.

In this paper, we propose a cognitive priority-based resource management (CPRM) scheme for cognitive femtocells in LTE systems with multimedia traffic. Assume that the femtocell adopts cognitive radio technology and serves both macro and femto users. The CPRM scheme dynamically adjusts the sensing duration of each subchannel to enhance the accuracy of channel sensing. According to sensing information, it chooses the unoccupied subchannels as data channels. In order to maximize system throughput and guarantee quality-of-service requirements, the CPRM scheme assigns a suitable priority value and the minimum number of transmission bits to each user based on its traffic type and urgency degree. Simulation results show that, the CPRM scheme can enhance the throughput of femto users by 10.2 % and 22.4 % as compared to the DBRM [7] and CRRM [8] schemes, respectively, without decreasing the throughput of macro users.

This paper proposes a new user-differentiation-based resource allocation (UDRA) scheme for OFDMA downlink systems. The UDRA scheme can strike a good balance between quality-of-service (QoS) requirement guarantee and system throughput enhancement, whereas conventional schemes cannot explicitly and accurately control this tradeoff. The UDRA scheme first performs a utility-based resource allocation algorithm for urgent users to fulfill QoS requirement, then it performs a maximum-sum-rate resource allocation algorithm for regular users to enhance system throughput. To achieve the balance between QoS guarantee and throughput enhancement, the UDRA scheme further designs a fuzzy logic user identifier (FLUI) to intelligently differentiate users from urgent and regular. Simulation results show that the proposed UDRA scheme enhances the system throughput by 23.1%, 14.1%, and 9.7%, compared to the PSRA [1], ARRA [2], and U-TMCR [3] schemes, respectively, under a QoS requirement guarantee.

Signaling overhead reduction has been a key approach to realizing energy-efficient wireless cooperative communication systems. Motivated by the fact that consecutive temporal samples of real-world wireless channels are typically correlated, this paper proposes to exploit such time-domain correlation for relay training overhead reduction in cooperative networks. Specifically, we consider a cooperative transmit beamforming system, in which relay terminals employing the decode-and-forward protocol collaboratively transmit the source message according to the pre maximal-ratio-combining (pre-MRC) principle. During the training phase, the relays send training signals to aid channel estimation at the destination. Based on the acquired record of the channel state information (CSI), the destination then employs a linear minimum-mean-square-errors (LMMSE) channel predictor to update the CSI; in this way, training overhead dedicated by relays can then be reduced. We derive a closed-form expression for the receive SNR at the destination when the pre-MRC beamforming factors are computed in accordance with the predicted CSI. Our analytic results can be used for characterizing the performance degradation of channel prediction as the duration of the prediction phase is enlarged. The proposed analytic studies are corroborated by numerical simulations.

In this paper, we investigate how to determine an optimal target channel sequence with the minimum cumulative handoff delay in cognitive radio networks. When the secondary user encounters multiple spectrum handoffs during its transmission period, the effects of multiple interruptions from the high-priority primary users and the traffic statistics of both the primary and the secondary users should be incorporated in the design of the optimal target sequence. The optimal target channel sequence can guild the secondary user to change its operating channel when the primary user's interruptions occur. With M candidate channels and L elements in the target channel sequence for spectrum handoffs, the exhaustive search (ES) requires time complexity of O(ML). In this paper, we propose a dynamic programming (DP) algorithm with time complexity of O(LM2) to determine an optimal target channel sequence. Furthermore, we propose a greedy algorithm with time complexity of O(M) and prove that the greedy algorithm only requires comparing six target channel sequences. Numerical results show that the low-complexity greedy algorithm can yield similar cumulative handoff delay performance as the optimal DP-based or ES-based algorithms in most cases except when the primary users' service time distributions at their operating channels are different.

In this paper, we study the Roadside Unit (RSU) placement problem in vehicular networks. We focus on the highway-like scenario in which there may be multiple lanes with exits or intersections along the road. In our model, each vehicle can access RSUs in two ways: 1) direct delivery, which occurs when the vehicle is in the transmission range of the RSUs, and 2) multi-hop relaying, which takes place when the vehicle is out of RSU transmission range. We account for both access patterns in our placement strategy and formulate this placement problem via an integer linear programming model such that the aggregate throughput in the network can be maximized. We also take into account the impact of wireless interference, vehicle population distribution, and vehicle speeds in the formulation. The performance of the proposed placement strategy is evaluated via ns-2 simulations together with VanetMobisim to generate vehicle mobility patterns. The results show that our strategy leads to the best performance as compared with the uniformly distributed placement and the hot spot placement. More importantly, our solution needs the least number of RSUs to achieve the maximal aggregate throughput in the network, indicating that our scheme is indeed a cost effective yet highly efficient placement strategy for vehicular networks.

In this paper, we propose a fuzzy Q-learning-based MIMO configuration mode and MCS level (FQL-MOMS) selection scheme for high speed packet access evolution (HSPA+) systems. The FQL-MOMS selection scheme intends to enhance the system throughput under the block error rate (BLER) requirement guarantee. It will determine an appropriate MIMO configuration mode and MCS (modulation and coding scheme) level for packet data transmission in HSPA+systems, under the situations that the channel status is varying and the channel quality indication (CQI) has report delay. The FQL-MOMS scheme considers not only the reported CQI and the last transmission result but also the BLER performance metric and the transmission efficiency. Moreover, it is effectively configured, where the fuzzy rules and the reinforcement signals for the Q-learning algorithm are sophisticatedly designed. Simulation results show that the proposed FQL-MOMS scheme increases the system throughput by up to 49.3 and 35.9 percent, compared to the conventional adaptive threshold selection (ATS) scheme [12] and the Q-HARQ scheme [14], respectively, under the BLER requirement fulfillment.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

In this paper, we present an analytical framework to evaluate the latency performance of connection-based spectrum handoffs in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs and the need of predetermining a set of target channels for spectrum handoffs. To quantify the effects of channel obsolete issue on the target channel predetermination, we should consider the three key design features: 1) general service time distribution of the primary and secondary connections; 2) different operating channels in multiple handoffs; and 3) queuing delay due to channel contention from multiple secondary connections. To this end, we propose the preemptive resume priority (PRP) M/G/1 queuing network model to characterize the spectrum usage behaviors with all the three design features. This model aims to analyze the extended data delivery time of the secondary connections with proactively designed target channel sequences under various traffic arrival rates and service time distributions. These analytical results are applied to evaluate the latency performance of the connection-based spectrum handoff based on the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard. Then, to reduce the extended data delivery time, a traffic-adaptive spectrum handoff is proposed, which changes the target channel sequence of spectrum handoffs based on traffic conditions. Compared to the existing target channel selection methods, this traffic-adaptive target channel selection approach can reduce the extended data transmission time by 35 percent, especially for the heavy traffic loads of the primary users.

In this paper, a utility-based fuzzy wavelength assignment with fiber delay line (UFWA) is proposed to achieve both high wavelength utilization and low traffic delay in optical burst switching (OBS) networks. It contains a suitable wavelength concentrator (SWC) to filter more adequate wavelengths with higher unities, several fuzzy wavelength evaluators (FWE) to calculate the adequate degrees of the filtered wavelengths, and a wavelength selector (WS) to determine the most adequate wavelength. Simulation results show that the UFWA not only achieves throughput higher than the preemptive latest available unused channel with void filling (PLAUC-VF) [6] and the efficient preemption-based channel scheduling algorithm (EPCS) [10] by about 300 Mbps and 150 Mbps, respectively.

The power-saving class of type II (PSC II), a type of sleep mode operations in the IEEE 802.16e standard, is designed to reduce power consumption for a mobile station with unsolicited grant service and real-time variable-rate connections. However, the configuration of fixed-length listening windows in the PSC II incurs unnecessary energy consumption or packet loss for these types of connections. In order to improve the energy efficiency of sleep mode operation, a cycle-based flexible window adjustment (FWA) approach for IEEE 802.16m systems is proposed in this paper. The FWA scheme dynamically adjusts the ratio of listening window to sleep window for each sleep cycle based on the number of both arrival and retransmission packets. Numerical analysis and simulation results show that the proposed FWA approach can achieve better energy conservation with reduced packet loss rate.

In this paper, we propose a hybrid automatic repeat request (HARQ) control scheme by using fuzzy Q-learning algorithm for high speed packet access evolution (HSPA+) systems. The challenge of the HARQ control in HSPA+systems is the selection of MIMO (multiple-input multiple-output) configuration mode and MCS (modulation and coding scheme) level for new data transmission of HARQ, under the situation that the channel quality indication has report delay. The fuzzy Q-learning-based HARQ (FQL-HARQ) scheme can determine an appropriate MIMO configuration mode and MCS level for new data transmission so as to maximize the system throughput under the guaranteed block error rate (BLER) requirement. Simulation results show that, the proposed FQL-HARQ scheme increases the system throughput by up to 70.09% as compared to the conventional adaptive threshold method.

In this paper, we propose an intelligent inter-ring route control, called fuzzy inter-ring route control with PRNN predictor (FIRC-P), for the bridged resilient packet rings (BRPR). The FIRC-P contains three functional blocks implemented by fuzzy logic systems or pipeline recurrent neural networks (PRNN): fuzzy bridge-node congestion indicator (FBCI), PRNN downstream-node fairness predictor (PDFP), and fuzzy route controller (FRC). The determination of the FIRC-P is based on the load balancing principle, in which the CW or CCW ringlet with lower congestion degree and higher service rate will be chosen. Simulation results show that the FIRC-P improves the performance by about 10% and 220% in packet dropping probability, by about 13% and 18% in average packet delay, and by about 6% and 19% in throughput over the queue length threshold route controller (QTRC) and the shortest path route controller (SPRC), respectively.

Generally, optimization techniques for resource allocation of OFDMA systems are infeasible for real-time applications. In this paper, we propose a genetic algorithm with subscriber station (SS) grouping resource allocation (GGRA) scheme for IEEE 802.16 uplink systems. The GGRA scheme firstly designs a rate assignment strategy, applied with a predefined residual lifetime, to allocate resource to each service dynamically. It then aggregates high correlation SSs into the same group, where the SSs will be allocated to different slots so as to avoid mutual user interference. Finally, the GGRA scheme finds an optimal assignment matrix for the system by the genetic algorithm, based on the SS groups to greatly lessen the computation complexity. The GGRA scheme can also maximize system throughput and fulfill QoS requirements. Simulation results show that the proposed GGRA scheme performs better than the EFS algorithm [] and the MLWDF algorithm [] in system throughput, voice/video packet drop rate, unsatisfied ratio of HTTP users/packets, and FTP throughput. The computation complexity of the GGRA scheme is also tractable and thus feasible for real-time applications.

This paper proposes a preference value-based cell selection (PVCS) scheme to satisfy quality of service (QoS) requirements, maximize accommodated number of calls, and minimize handoff occurrence frequency in heterogeneous wireless networks. The PVCS scheme contains three stages including candidate cells selection, preference value calculation, and target cell determination. The candidate cells selection is used to sift the suitable cells for the call request. All suitable cells form a candidate cell set. The preference value calculation computes the preference value of each cell in the candidate cell set, which is optimized by considering QoS factor, loading factor, and mobility factor, for the purpose of maintaining QoS requirements of a call request, maximize accommodated number of calls, and minimizing handoff occurrence frequency. Eventually, the candidate cell, which has the maximum preference value, would be selected for the call request. Simulation results show that PVCS scheme achieves higher total throughput than UGT while satisfying the QoS requirements. It also has lower new call blocking rate and handoff call dropping rate than UGT, which means more accommodated calls than UGT. Besides, PVCS reduces the total handoff occurrence frequency by about 20% as compared to UGT.

The purpose of this paper is to reduce the complexity of scheduling scheme for OFDMA/SDMA system with multimedia traffic. To achieve this purpose, we propose a low-complexity beamforming-based scheduling (LCBS) scheme to solve the multi-dimensional (space, time, and frequency) radio resource allocation problem in OFDMA/SDMA systems. By utilizing a semi-orthogonal user selection (SUS) algorithm, the LCBS scheme has a good trade-off between the implementation complexity and system performance in throughput, QoS guarantee (for all traffics), and fairness guarantee (for non-real-time and best effort traffics). Simulation results show that the LCBS scheme can efficiently improve the fairness performance compared to most existing algorithms.

This paper proposes a nonlinear-predictive QoS-promoted dynamic bandwidth allocation (NQ-DBA) scheme for upstream transmission in Ethernet passive optical network (EPON) systems. The proposed NQ-DBA scheme divides incoming packets of voice, video, data service into six priorities, whereas packets having less room before QoS requirements violation will be dynamically promoted to high priority. It predicts packets arriving at a prediction interval for ONUs using a nonlinear pipelined structure of recurrent neural network (RNN) with extended recursive least squares (ERLS) update so that the bandwidth allocation can be more up-to-date and then accurate. Simulation results show that the proposed NQ-DBA scheme achieves higher system utilization than the DBAM scheme by 4%, and 21%, 90%, 43%, respectively.

In this paper we propose an economized-CSI (channel state information) opportunistic scheduling (ECOS) scheme for OFDMA/TDD downlink systems. The ECOS scheme consists of a quality-of-service (QoS) guarantee scheduling (QGS) algorithm and a CSI overhead reduction (COR) algorithm. The QGS algorithm guarantees QoS requirements by adjusting priority value and anticipated rate of users dynamically. In addition, it achieves balance between QoS guarantee and throughput maximization by employing a priority threshold. The COR algorithm performs admissible user selection to economize the amount of CSI overhead. Since all users do not need to feed back CSI every frame, the transmission power used for CSI reporting is economized. Besides, the COR algorithm can reduce the computation complexity of the QGS algorithm because the number of users to be scheduled is reduced. Simulation results show that the proposed ECOS scheme greatly reduces the uplink bandwidth occupancy of CSI feedback. Also, it achieves high system throughput and maintains QoS guarantee at high traffic load.

In this paper, a utility-based throughput maximization (UTM) scheduling scheme is proposed for downlink multiuser MIMO-OFDMA systems. The UTM scheme allocates subchannels, receiving antenna, and modulation order to multimedia users with goals to maximize throughput and guarantee QoS requirements. Based on the channel quality and the QoS requirements of each user, we formulate the scheduling into an optimization problem subject to system constraints and propose a heuristic TM algorithm to solve the optimization problem. Simulation results show that the UTM scheme achieves system throughput higher than conventional schemes such as ARRA [8] and CDPS [7] schemes by about 8% and 21% at traffic intensity 0.75, respectively. Moreover, the UTM scheme has better QoS satisfaction ratio than the ARRA and CDPS schemes.

In this paper, a fuzzy Q-learning-based hybrid automatic repeat request (FQL-HARQ) scheme is proposed to enhance the system performance of high speed downlink packet access (HSDPA) in universal mobile telecommunications system (UMTS). In the HSDPA system, it is an important issue to choose modulation and coding scheme (MCS) for new data packet when channel quality indicator (CQI) is inaccurate. Hence, the purpose of the FQL-HARQ scheme is to determine a suitable MCS for new data packet so as to maximize the system throughput while guarantee the block error rate (BLER) requirement. In the FQL-HARQ scheme, the fuzzy logic is used to choose a suitable MCS for each new data packet. According to the feedback information from the HSDPA system, the Q-learning algorithm is adopted to update fuzzy rule base. This makes the FQL-HARQ scheme can adapt to the variation of environment. Simulation results show that the proposed scheme can increase the system throughput of up to 70% compared to the conventional adaptive method in the case with CQI report delay.

The paper proposes a utility function and game theory (UGT) based network selection scheme to maximize accommodated number of calls, minimize handoff occurrences frequency, and fulfill quality of service (QoS) requirements in heterogeneous wireless networks. When a new call or a handoff call arrives, UGT would calculate the utility value and preference value for each candidate network based on the QoS satisfaction of the call request and the cooperative game computation. Finally, the network, which has the maximum result of linearly combined utility value and preference value, would be selected as the most suitable network for the call request. Simulation results show that UGT has good QoS satisfaction. It also allows system to accommodate more calls than iterative TOPSIS scheme. In addition, UGT can reduce the handoff occurrence frequency by about 30% as compared to iterative TOPSIS.

In this paper, admission control by a fuzzy Q-learning technique is proposed for WCDMA/WLAN heterogeneous networks with multimedia traffic. The fuzzy Q-learning admission control (FQAC) system is composed of a neural-fuzzy inference system (NFIS) admissibility estimator, an NFIS dwelling estimator, and a decision maker. The NFIS admissibility estimator takes essential system measures into account to judge how each reachable subnetwork can support the admission request's required QoS and then output admissibility costs. The NFIS dwelling estimator considers the Doppler shift and the power strength of the requested user to assess his/her dwell time duration in each reachable subnetwork and then output dwelling costs. Also, in order to minimize the expected maximal cost of the user's admission request, a minimax theorem is applied in the decision maker to determine the most suitable subnetwork for the user request or to reject. Simulation results show that FQAC can always maintain the system QoS requirement up to traffic intensity of 1.1 because it can appropriately admit or reject the users' admission requests. Also, the FQAC can achieve lower blocking probabilities than conventional JSAC proposed in and can significantly reduce the handoff rate by 15-20 percent.

The paper proposes a cellular neural network and utility (CNNU)-based radio resource scheduler for multimedia CDMA communication systems supporting differentiated quality-of-service (QoS). Here, we define a relevant utility function for each connection, which is its radio resource function weighted by a QoS requirement deviation function and a fairness compensation function. We also propose cellular neural networks (CNN) to design the utility-based radio resource scheduler according to the Lyapunov method to solve the constrained optimization problem. The CNN is powerful for complicated optimization problems and has been proved that it can rapidly converge to a desired equilibrium; the utility-based scheduling algorithm can efficiently utilize the radio resource for system, keep the QoS requirements of connections guaranteed, and provide the weighted fairness for connections. Therefore, the CNNU-based scheduler, which determines a radio resource assignment vector for all connections by maximizing an overall system utility, can achieve high system throughput and keep the performance measures of all connections to meet their QoS requirements. Simulation results show that the CNNU-based scheduler attains the average system throughput greater than the EXP and the HOLPRO scheduling schemes by an amount of 23% and 33%, respectively, in the QoS guaranteed region.

The wireless mesh network (WMN) is a promising solution to support wireless broadband applications. However, mesh networks face the power unfairness issue. Compared to the users far away from the gateway, the users near the gateway have to relay more traffic and consume more power. This paper proposes a scalable ring-based WMN that can ensure power fairness among users by adjusting its ring widths. On top of the ring-based cell structure, frequency planning is suggested to reduce the contending users, thereby making the system more scalable to accommodate more users. To investigate the overall tradeoffs among power fairness, capacity, and coverage, we develop an analytical model to evaluate the throughput and power consumption of the ring-based WMN using carrier sense multiple access (CSMA) medium access control (MAC) protocol in the unsaturated situation. Then, the optimization approach is applied to determine the best number of rings and the optimal ring widths, aiming to maximize cell capacity and coverage subject to the requirement of power fairness. Numerical results show that compared to the uniform ring-width strategy, the variable ring-width design criterion can improve cell capacity and coverage.

A centralized QoS guaranteed throughput enhancement (QoS GTE) scheduling scheme for downlink relay-assisted WiMAX networks is proposed. The QoSGTE consists of a transmission time based path selection algorithm (TTPSA), a service order based resource allocation algorithm (SO RAA), and a transmission concurrency decision algorithm (TCDA). The TT_PSA selects the path with the minimal transmission time for packets, which takes path loss, shadow fading, and interference into consideration. The SO RAA gives high priority to urgent users and maximizes throughput under QoS satisfaction. The TCDA carries out flexible resource reuse by deciding which relay station (RS) can transmit concurrently using the same frequency and time slots. Simulation results show that the proposed QoSGTE outperforms the scheme of without relay by an amount of 63% and that of the LMP by an amount of 28%, in the system throughput. The QoS GTE can also guarantee QoS requirements of real time services by 97%.

In this paper, a dynamic priority-based resource allocation (DPRA) scheme is proposed for uplinks in IEEE 802.16 wireless communication systems. The goal of the DPRA scheme is to maximize the system throughput while satisfying diverse QoS requirements. Four types of multimedia traffic are considered, where these multimedia traffic are given urgency degrees via dynamic priority values, and the radio resources are allocated according to their priority values. Also the DPRA scheme performs consistent allocation for packets with multimedia traffic in aspects of subchannel, modulation order, and power. Simulation results show that the proposed DPRA scheme outperforms the conventional algorithm in system throughput by an amount of 15%. The rtPS packet dropping rate of DPRA scheme is 70% lower than other schemes and satisfies QoS requirements. Besides, the proposed DPRA scheme can achieve less computational complexity and lower transmission overhead.

Relay stations (RSs) are usually used to enhance the signal strength for the users close to the cell boundary. However, transmission through a relay station needs two transmission phases, i.e., one is from the base station to the relay station and the other is from relay station to mobile stations. Thus, relay may also decrease system capacity if two-phase transmission time is considered. As a result, whether or not data are transmitted by one-hop or two-hop transmission should be determined based on both signal strength and throughput. In this paper, we investigate the optimal relay location aiming to maximize system capacity. We consider two relay selection rules for determining whether a two-hop transmission is necessary: signal strength-oriented and throughput-oriented. We find that the signal strength-oriented two-hop transmission may yield even lower system capacity than the one-hop transmission. Based on the throughput-oriented rule, we find that the throughput in the two-hop transmission can be higher than that in the one-hop transmission at some locations. We also identify the optimal relay location that can achieve the highest system capacity.

The letter proposes a method to analyze the system capacity of uplinks with imperfect SIR-based power control in direct sequence/code division multiple access (DS/CDMA) cellular systems with the multipath fading. It considers the received signal-to-interference ratio (SIR) as a random variable due to imperfect SIR-based power control. Based on assumptions regarding the average received SIR, a set of linear equations for the average received power on each uplink is derived. An outage-based system capacity can be then obtained according to the feasibility of the average received power vector and the average bit error rate. Finally, by applying central limit theorem, a closed-form solution for system capacity is approximately derived. Results show that numerical results of the closed-form solution have a good match with simulation results, which supports the validity of the proposed capacity analysis method.

This paper proposes an adaptive radio resource allocation (ARRA) algorithm for downlink OFDMA/SDMA systems with multimedia traffic. Considering multiple service classes and diverse QoS requirements of multimedia traffic, the ARRA algorithm is designed with the goal to maximize spectrum efficiency and to fulfill quality of service (QoS) requirements. It is composed of two parts, a dynamic priority adjustment (DPA) scheme and a priority-based greedy (PBG) scheme. The DPA adopts a type of time-to-expiration to indicate the degree of user's urgency, and dynamically gives high priorities to urgent users. The PBG allocates the radio resource iteratively, based on a cost value, to maximize the system throughput while allocating enough resource to high-priority users. Simulation results show that the ARRA algorithm outperforms conventional algorithms in terms of system throughput and satisfaction extent of QoS requirements; it can sustain users' QoS requirements up till a traffic load of 0.8, while the conventional algorithms cannot guarantee QoS requirements after a traffic load of 0.3.

In this paper, we present an enhanced adaptive frequency hopping (EAFH) mechanism for improving the performance of frequency hopping-based wireless personal area networks (WPANs) under frequency-static and frequency-dynamic interference. The proposed mechanism monitors the overall packet error rate (PER) of the system to determine the right number of channels to be excluded from the hopset. Then based on the PER of individual channel, it decides whether to exclude a certain channel or not. Finally, proper packet length is associated with those channels remaining in the hopset. These decisions, which pertain to hopset size and packet length, are made so as to optimize the performance of the hopping system in a coexistence environment. We developed an analytical model to justify the behavior and performance of the proposed mechanism. Simulations are conducted under an environment of some collocated Bluetooth (BT) piconets and a Wi-Fi network to validate the developed model and show the superiority of EAFH. Simulation results show that, compared with those existing mechanisms including orthogonal hopset-based mechanisms, EAFH could provide much higher throughput while still maintaining reasonably good channel occupancy.

With the goal to provide ubiquitous wireless Internet access, large-scale deployment of outdoor public access networks will continue at a rapid pace in the next years. In outdoor environment, connecting access points (APs) via cables is costly and requires a lot of cabling engineering work. Therefore, the multihop-augmented network architecture has become an option of upsurging interest. This paper investigates how to deploy APs in an outdoor public access network to improve throughput and quality-of-service (QoS) while extending the coverage by multihop communications. Frequency planning is suggested to improve user throughput with QoS provisioning, and to make the multihop network more scalable to facilitate coverage extension. To investigate the overall tradeoffs among QoS, throughput, and coverage, we develop an analytical throughput and delay model for this multihop network using the carrier sense multiple access (CSMA) medium access control (MAC) protocol. This model also considers bidirectional asymmetric traffic for users operating in the unsaturated situation. Then, we apply an optimization approach to determine the optimal number of APs in a cluster and the best separation distances between APs, aiming to maximize the capacity and coverage of a cluster of APs subject to the delay requirement.

The paper proposes an adaptive radio resource allocation (ARRA) algorithm for downlink OFDMA/SDMA systems with goals to achieve quality of service (QoS) satisfied and throughput maximized. The ARRA algorithm considers multiple service classes of multimedia traffic and their diverse QoS requirements. It contains two parts, a dynamic priority adjustment scheme and a priority-based greedy (PBG) algorithm. The dynamic priority adjustment scheme gives high priority to urgent users and dynamically adjust the value frame by frame based on users' QoS requirement and queue occupancy. The PBG algorithm allocates the radio resource iteratively according to a cost value to maximize the system throughput while allocating enough resource to high-priority users. Simulation results show that the ARRA algorithm outperforms the conventional algorithms in terms of system throughput under the satisfaction of QoS requirements.

WCDMA/WLAN interworking networks are considered to support high-data-rate multimedia services and high mobility. In this paper, we propose a utility function-based access selection method (UFAS) to determine which cell is most suitable for a mobile node's service request in heterogeneous WCDMA and WLAN networks. The UFAS method contains three parts, cells classification, utility function computation, and target cell determination. This utility function is composed of four evaluation functions, and is designed with a balancing index which can reduce the utility value of a cell if the cell is heavily loaded. Simulation results show that the UFAS method achieves larger system throughput than a policy-enabled (PE) handoff decision method (Wang, 1999). Moreover, the UFAS method can reduce handoff occurrence rates and strike a balance between system utilization and cell loading while maintaining QoS requirements.

In this paper, a Q-learning-based hybrid automatic repeat request (Q-HARQ) scheme is proposed to achieve efficient resource utilization for high speed downlink packet access (HSDPA) in universal mobile telecommunications system (UMTS). The hybrid ARQ procedure is modeled as a discrete-time Markov decision process (MDP), where the transmission cost is defined in terms of the signal-to-interference-and-noise (SINR) which is based on the desired (quality-of-service) QoS parameters of transport block error rate (BLER) for enhancing spectrum utilization subject to QoS constraint. The Q-learning reinforcement algorithm is employed to accurately estimate the transmission cost to perform the most suitable decision of modulation and coding scheme for the packet initial transmission while the requirement of transport block error rate is guaranteed. Simulation results show that the QoS requirement of block error rate for Q-HARQ is nearly met around a reasonable value indeed. In addition, the system throughput of the Q-HARQ can be improved under the specific QoS constraint of BLER. It is verified finally that the Q-HARQ scheme is feasible in the practical system because of the short processing and convergence time.

The wireless mesh network (WMN) is an essential low-power solution to support ubiquitous broadband services. However, mesh networks face the power unfairness and throughput bottleneck issues. Compared to the users far away from the gateway, the users near the gateway need to relay more traffic and consume more power. This paper proposes a scalable ring-based WMN that can ensure power fairness among users by adjusting the ring widths. On top of the ring-based WMN, frequency planning is suggested to overcome the throughput bottleneck issue for the inner-ring users near the gateway, thereby making the system more scalable to accommodate more users and facilitating coverage extension. We also investigate the overall tradeoffs of the ring-based WMN in terms of power fairness, capacity, and coverage. An analytical model is developed to evaluate the throughput and power consumption of the ring- based WMN using carrier sense multiple access (CSMA) MAC protocol in the unsaturated situation. Then, a mixed-integer nonlinear optimization problem aiming to maximize cell capacity and coverage subject to the constraint of power fairness is formulated. Applying this optimization approach, we obtain the optimal number of rings and the associated ring widths of the ring-based WMN.

This paper proposes a maximum freedom last (MFL) scheduling algorithm for downlinks, from the roadside unit to the onboard unit (OBU), of dedicated short-range communication networks in intelligent transportation systems, to minimize the system handoff rate under the maximum tolerable delay constraint. The MFL scheduling algorithm schedules the service ordering of OBUs according to their degree of freedom, which is determined by factors such as remaining dwell time of service channel, remaining transmission time, queueing delay, and maximum tolerable delay. The algorithm gives the smallest chance of service to the OBU with the largest remaining dwell time, the smallest remaining transmission time, and the largest weighting factor, which is a function of the queueing delay and the maximum tolerable delay. Simulation results show that the MFL scheduling algorithm outperforms the traditional first-come-first-serve and earliest-deadline-first methods in terms of service failure and system handoff rates

The paper proposes and analyzes a novel adaptive p-persistent-based (APP) medium access control (MAC) scheme to provide low delay variance for IEEE 802.11 WLAN. The APP MAC scheme can differentiate permission probabilities of transmission for stations being incurred with different packet delays, and the permission probability is designed as a function of the numbers of retransmissions and re-backoffs so that stations being with larger packet delay can attain higher permission probability. Additionally, the scheme is modeled by a Markov-chain and successfully analyzed, where the system throughput and delay are derived. Results show that the proposed APP MAC scheme can achieve lower delay variance than conventional algorithms; it can effectively reduce the collision probability and the mean delay, therefore, the mean throughput is enhanced, as well.

Wireless mesh network (WMN) is a viable solution to support ubiquitous broadband services with low transmission power. This paper investigates the tradeoffs among delay, throughput, and coverage in a multi-channel ring-based wireless mesh network. In the proposed WMN, a simple ring-based frequency planning is employed to effectively utilize available channels. To evaluate the delay and throughput in the proposed WMN, we develop a physical (PHY)/medium access control (MAC) cross-layer analytical framework, which incorporates the carrier sense multiple access (CSMA) MAC protocol and a physical-layer distance-based rate adaptation with multi-hop connections. On top of the analytical model, the mixed-integer nonlinear programming (MINLP) optimization approach is applied to analytically determine the optimal number of rings and the associated ring widths, aiming at maximizing the coverage of a mesh cell subject to the requirements of throughput and delay.

The paper proposes a neural fuzzy call admission and rate controller (NFARC) scheme for WCDMA cellular systems providing multirate services. The NFARC scheme can guarantee the quality of service (QoS) requirements and improve the utilization of the system. Simulation results show that the NFARC scheme achieves low forced termination probability and high system capacity even in the bursty traffic conditions. NFARC accepts users more than intelligent call admission controller (ICAC) by an amount of 45.35%.

This paper proposes an efficient radio resource allocation (RRA) mechanism in multirate mixed-size WCDMA cellular systems. The proposed RRA mechanism deals with power and rate allocation for non-handoff and handoff users sepa-rately. For non-handoff users, RRA mechanism use a novel multi-qualify balancing power algorithm and mutirate re-moval algorithm to meet a common balanced signal quality for all cells. Also, for soft handoff users, RRA mechanism has a joint power and rate allocation (JPRA) algorithm to determine an appropriate allocation of transmission power and service rate for multirate soft handoffs. Via the JPRA algorithm for soft handoff, the system can achieve power bal-ancing among cells. Simulation results show that the JPRA algorithm has better cell’s service coverage and higher sys-tem capacity.

Adaptively adjusting transmission rate and power to concurrently enhance goodput and save energy is an important issue in the wireless local area network (WLAN). However, goodput enhancement and energy saving are two contradictory goals, since high data rate usually requires high transmission power to meet the bit-error-rate (BER) requirement. To achieve the tradeoff between energy efficiency and goodput, therefore, we suggest a fast channel-driven rate and power adaptation (CDRPA) algorithm. Furthermore, we develop a physical (PHY)/medium access control (MAC) cross-layer analytical method to evaluate goodput and energy efficiency, incorporating the impacts of Nakagami fading channel and the carrier sense multiple access (CSMA) MAC protocol. This paper compares two adaptation

The wireless mesh network (WMN) is an economical solution for disseminating broadband wireless information in the intelligent transportation systems (ITS). This paper investigates the issue of deploying access points (APs) in an ITS wireless mesh network, where several adjacent APs form a cluster. Each AP in a cluster operates as a wireless relay to forward neighboring AP's traffic to the central access point connected to the Internet through cables. In general, access points are placed for maximizing cell coverage. However, larger coverage of an AP leads to lower throughput and longer delay in the access link as well as in the relay link. To find the optimal tradeoffs among delay, capacity, and coverage, we develop a physical (PHY)/medium access control (MAC) cross-layer analytical model to evaluate the throughput and delay of the considered ITS wireless mesh network. We consider the carrier sense multiple access (CSMA) protocol and the impact of hop distance on the data rate in the physical layer. Then, we apply the mixed-integer nonlinear programming (MINLP) optimization approach to determine the optimal number of APs in a cluster and the best cell radius of each AP, aiming at maximizing the capacity and coverage of a cluster of APs subject to the delay and fairness requirements.

Transmitter-based signal pre-processing is investigated for the two-dimensional (2-D) OFDM-CDMA (orthogonal frequency division multiplexing, code division multiple access) forward link systems, aiming to increase the system sum-capacity and shift the signal processing complexity of a mobile unit to the base station. For the non-selective fading channels, the optimum pre-processing that achieves the maximum ergodic sum-capacity is obtained by applying the principle of multi-user water filling under the condition of zero multiple access interference (MAI). For the selective fading channels, on the other hand, three sub-optimum methods including ZF (zero-forcing) with power normalization, MMSE (minimum mean square error), and the newly proposed ZF with multi-user water filling are investigated under the constraint of total transmit power. Numerical results show that: (i) The new pre-processing outperforms the traditional other two in terms of achievable sum-capacity, (ii) 2-D spreading pattern should be adapted to the channel conditions so as to minimize MAI and thus maximize the sum-capacity. In this regard, 2-D spreading has advantage over the 1-D one. (iii) The fewer the users scheduled at a time, the higher the system capacity; a result obtained from exploiting the multi-user diversity.

In recent years, the IEEE 802.11 wireless local area networks (WLANs) have become a very popular access network technique. However, due to the large handoff latency, most application scenarios are limited to hot spot areas without handoff across different access points (APs). As the advance of many real-time or highly interactive applications such as voice over IP (VoIP), one of burning issues for the WLAN is to reduce the handoff latency. Current studies in the literature have indicated that the latency in searching available APs dominates the handoff latency. Thus, we propose a simple analytical model to calculate the search latency and success probability for the handoff in the IEEE 802.11 WLAN. A new performance metric, namely the "effective search time (EST)", is introduced to evaluate the latency that is required for a station to successfully search a channel from neighboring APs. Based on the developed model, the optimum numbers of the probe request and response transmission for minimizing the EST can be obtained. Our results demonstrate that, in the environment of 100 contending stations, the EST with optimum number of transmissions can be reduced by 30% compared to that in the legacy IEEE 802.11 WLAN.

The wireless mesh network (WMN) is an economical solution to support ubiquitous broadband services. This paper investigates the tradeoffs among quality-of-service (QoS), capacity, and coverage in a scalable multichannel ring-based WMN. We suggest a simple frequency planning in the proposed ring-based WMN to improve the capacity with QoS support, and to make the system more scalable in terms of coverage. We develop a physical (PHY)/medium access control (MAC) cross-layer analytical model to evaluate the delay, jitter, and throughput of the proposed WMN, by taking account of the carrier sense multiple-access (CSMA) MAC protocol, and the impact of hop distance on transmission rate in the physical layer. Furthermore, the mixed-integer nonlinear programming optimization approach is applied to determine the optimal number of rings and the associated ring widths, aiming at maximizing the capacity and coverage of a mesh cell subject to the delay requirement

To balance the time-varying traffic load between cells, caused by user mobility and diverse applications, it is crucial for next-generation code-division multiple-access (CDMA) cellular networks to configure cell coverage and capacity dynamically. In this paper, we show that pilot power allocation is highly coupled to other facets of radio resource management. We propose a novel dynamic cell configuration scheme for multimedia CDMA cellular networks, based on reinforcement-learning, which takes into account pilot, soft handoff, and maximum link power allocations, as well as call admission control mechanisms. Simulation results demonstrate the effectiveness of the proposed scheme in situation-aware CDMA networks.

This paper proposes a channel effect prediction based power control scheme using pipeline recurrent neural network (PRNN)/extended recursive least squares (ERLS) for uplinks in direct sequence code division multiple access (DS-CDMA) cellular mobile systems. Conventional signal-to-interference (SIR) prediction-based power control schemes may incur prediction mistakes caused by the adjustment of transmission power. The proposed power control scheme purely tracks the variation of channel effect and, thus, can be immune to any power adjustment. Furthermore, it adopts the PRNN with ERLS for predicting the channel effect. Simulation results show that the channel effect prediction-based power control scheme using PRNN/ERLS achieves a 40% higher system capacity and a lower outage probability than the conventional SIR prediction-based power control scheme using grey prediction method (IEEE Trans. Veh. Technol., Vol. 49, No. 6, p. 2081, 2000).

This paper proposes a novel situation-aware data access manager using fuzzy Q-learning technique (FQ-SDAM) for multi-cell WCDMA systems. The FQ-SDAM contains a fuzzy Q-learning-based residual capacity estimator (FQ-RCE) and a data rate scheduler (DRS). The FQ-RCE can accurately estimate the situation-dependent residual system capacity, and appropriately chooses the received interference powers from the home-cell and adjacent-cell as input linguistic variables, which simplifies the multi-cell environment into a single-cell environment by applying a perceptual coordination mechanism. The DRS can effectively allocate the resource for non-real-time terminals by modifying the exponential rule (S. Shakkottai and A.L. Stolyar, 2001), which considers the effect of interference on adjacent cells. Simulation results show that, compared to the link and interference-based demand assignment (LIDA) scheme proposed in S. Kumar and S. Nanda (1999), FQ-SDAM can effectively reduce the packet error probability and improve aggregate throughput of the non-real-time services in both the homogeneous and non-homogeneous multi-cell WCDMA environments. Additionally, the modified exponential rule achieves better system performance than the original exponential rule.

This work presents a new scheme which exploits differentiations of both inter frame space (IFS) and contention window (CW) to achieve weighted fairness for two classes of services under enhanced distributed channel access (EDCA) mode in an 802.11e WLAN. Given the arbitration IFSs (AIFSs), the proposed scheme can properly set the corresponding CWs such that the ratio of the two classes' successful transmission probabilities can attain a pre-defined weighted-fairness goal. Additionally, the throughput and delay of the two classes of services are derived. The simulation results compare well with the analytical results. Also, the analytical results illustrate that the proposed scheme can achieve a fairness index very close to 1 and attain a greater aggregate throughput with lower collision probability than the CW differentiation scheme presented in A. Banchs and X. Perez (2002), for a specified weighted-fairness goal.

In this paper, we propose two intelligent leaky bucket algorithms for sustainable-cell-rate usage parameter control of multimedia transmission in asynchronous transfer mode networks. One is the fuzzy leaky bucket algorithm, in which a fuzzy increment controller (FIC) is incorporated with the conventional leaky bucket algorithm; the other is the neural fuzzy leaky bucket algorithm, where a neural fuzzy increment controller (NFIC) is added with the conventional leaky bucket algorithm. Both the FIC and the NFIC properly choose the long-term mean cell rate and the short-term mean cell rate as input variables to intelligently determine the increment value. Simulation results show that both intelligent leaky bucket algorithms have significantly outperformed the conventional leaky bucket algorithm, by responding about 160% faster when taking control actions against a nonconforming connection while reducing as much as 50% of the queueing delay experienced by a conforming connection. In addition, the neural fuzzy leaky bucket algorithm outperforms the fuzzy leaky bucket algorithm, in aspects of three performance measures such as selectivity, responsiveness, and queueing delay, especially when the traffic flow is bursty, dynamic, and nonstationary.

In this paper, we propose a traffic conditioner with promotion and fairness guarantee (TC/spl I.bar/PFG) schemes to amend the inappropriate marking by conventional traffic conditioners. The TC/spl I.bar/PFG is based on the two rate three color marker (TRTCM) scheme. It can allocate appropriate bandwidth to micro-flow to reduce the unfair distribution among micro-flows and improve the utilization by allowing promotion of packets when there is available bandwidth. Simulation results show that TC/spl I.bar/PFG outperforms TRTCM; the former achieve higher fairness among micro-flows and better throughput than the latter.

In this paper, a link gain prediction-based power control scheme is designed for DS-CDMA cellular mobile systems. The link gain prediction can help to remove the interference of the power control adjustment itself. The pipeline recurrent neural network (PRNN) with extended recursive least square (ERLS) is adopted for the prediction. This PRNN/ERLS predictor possesses infinite memory of past signals so that it can capture precisely the signal correlation and improve the delay compensation of power control. Simulation results show that the link gain prediction-based power control scheme using PRNN/ERLS has lower outage probability than the received SIR prediction-based power control scheme using grey prediction method [S. L. Su, Y. C. Su, and J. F. Huang, November 2000] and improves the system capacity by an amount of 41.6%.

This paper investigates different power allocation algorithms for downlink handoff in the wideband code division multiple access system from both outage performance and power efficiency perspectives. Traditionally, the site selection diversity transmission (SSDT) scheme has been viewed as the optimal solution for downlink handoff from the standpoint of power efficiency, which selects the best base station to transmit downlink signals to the user. However, from the outage performance perspective, the user at the cell boundary may have poor quality if there is only one serving base station. Thus, we are motivated to develop an improved SSDT scheme that can take both power efficiency and outage performances in account. In this paper, we propose a joint link proportional power allocation (LPPA) and SSDT scheme. Through analysis and simulation, we demonstrate that the proposed joint LPPA and SSDT scheme is the optimal power allocation solution for downlink handoff in the WCDMA system, which not only can improve the outage performance over the SSDT scheme when the traffic intensity is low, but can be very power efficient as the SSDT scheme in the heavy traffic situation.

Handover in heterogeneous cellular networks is one of the hottest topics for the wireless networks beyond the third generation. This paper presents a new downlink soft handover algorithm to solve the serious "power exhausting" problem during the traditional handover process in heterogeneous cellular systems. The power exhausting problem occurs when in the traditional handover process, the serving base stations usually adopt the equal power allocation (EPA) strategy for different sizes of cells. Thus we propose a constrained unequal power allocation (CUPA) technique to serve handover users by taking account of the effects of different cell sizes and shadowing. Compared to the EPA method, our simulation results show that the proposed soft handover algorithm combining with the CUPA technique can improve total system capacity by 5% in a homogeneous cellular system, and 73% in a heterogeneous cellular system.

Multiuser detection (MUD) can increase the system capacity. Wideband code division multiple access (WCDMA) systems adopt this new technology, but the capacity limitation still exists due to other cells' multiple access interference (MAI). We propose a fuzzy call admission control (FCAC) for WCDMA using MUD with successive interference cancellation (SIC). The FCAC considers both intra-cell and inter-cell MAI, and it is designed to maximize the benefit of MUD. Simulation results show that the FCAC with MUD can provide more accurate management of system resource than FCAC with RAKE receiver and SIR-based CAC with MUD in WCDMA systems.

A Q-learning-based multirate transmission control scheme (Q-MRTC) for radio resource control (RRC) in WCDMA systems is proposed. The RRC problem is modelled as a semi-Markov decision process (SMDP). We successfully apply a real-time reinforcement learning algorithm, named Q-learning, to accurately estimate the transmission cost for the multi-rate transmission control. For the cost function approximation, we apply the feature extraction method to map the original state space into a more compact set which represents the resultant interference profile. Simulation results show that the Q-MRTC can achieve higher system throughput and better users' satisfaction index, by an amount of 87% and 50%, respectively, than the interference-based multi-rate transmission control scheme, while keeping the QoS requirement.

A guard-interval based symbol and frequency synchronization technique with an extended observation interval of the OFDM transmission with higher level modulations is investigated for the wireless radio environments. The performance of the proposed guard-interval based symbol and frequency synchronization is evaluated by the computer simulations.

The paper proposes congestion control using fuzzy/neural techniques for integrated voice and data direct-sequence code division multiple access/frame reservation multiple access (DS-CDMA/FRMA) cellular networks. The fuzzy/neural congestion controller is constituted by a pipeline recurrent neural network (PRNN) interference predictor, a fuzzy performance indicator, and a fuzzy/neural access probability controller. It regulates the traffic input to the integrated voice and data DS-CDMA/FRMA cellular system by determining proper access probabilities for users so that congestion can be avoided and the throughput can be maximized. Simulation results show that the DS-CDMA/FRMA fuzzy/neural congestion controllers perform better than conventional DS-CDMA/PRMA with channel access function in terms of voice packet dropping ratio, corruption ratio, and utilization. In addition, the neural congestion controller outperforms the fuzzy congestion controller.

We propose a fuzzy/neural congestion controller for DS-CDMA/FRMA cellular systems. The congestion controller adopts fuzzy logic and neural network techniques to regulate the transmission permission probability of contention users, according to the predicted interference and indicated performance. It contains a pipeline recurrent neural network (PRNN) interference predictor, a fuzzy performance indicator, and a fuzzy permission probability controller. Simulation results show that the fuzzy/neural congestion control for DS-CDMA/FRMA is more adaptive and has lower corruption ratio by 36.67% and smaller voice packet dropping ratio by 15.15%, compared to the channel access function for DS-CDMA/PRMA proposed by Brand and Aghvami (see IEEE J. Select. Areas Commun., vol.14, no.9, p.1698-1707, 1996).

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract.

This paper presents the design of a fuzzy usage parameter controller (FUPC) for ATM (asynchronous transfer mode) networks. The usage parameter control (UPC) is here based on a dual leaky bucket (DLB) mechanism, with parameters of token generation rate and token pool size. In FUPC, we further employ parameters of token-change rate and interarrival time of incoming cells as input linguistic variables to reflect the usage situation in advance. Simulation results reveal that the proposed FUPC possesses a shorter response time to detect violation cells, keeps the output rate of the user close to the declared usage parameters, and thus achieves more precise control than DLB.

ATM (asynchronous transfer mode) networks support services with bursty traffic. A sophisticated and real-time admission controller is needed not only to guarantee the required quality-of-service (QoS) for existing calls but also to achieve higher system efficiency. In this paper, we propose a power-spectrum based neural fuzzy connection admission control (PNFCAC) mechanism for an ATM network. The power spectrum of the input process explicitly describes the correlation behavior of input traffic and has a great impact on the queueing behavior of an ATM network. The neural fuzzy controller has been successfully applied to deal with the complex problem which can not be analyzed by classical methods based on probability theory and bivalent logic. Under the constraint of QoS, we construct a decision hyperplane of connection admission control according to the parameters of the power spectrum. We further adopt the learning/adapting capabilities of the neural network to adjust the optimum location of the boundary between these two decision spaces. Simulation results show that the PNFCAC mechanism provides a superior system utilization over the conventional CAC schemes and the neural fuzzy CAC using time-domain analysis, while keeping the QoS contract.

ATM (asynchronous transfer mode) systems can support services with bursty traffic. An ATM system needs a sophisticated and real-time connection admission controller not only to guarantee the required quality-of-service (QoS) for existing calls but also to raise the system efficiency. The input process has a power-spectral-density (PSD) which explicitly contains the correlation behavior of input traffic and has a great impact on the system performance. Also, a neural network has been widely applied to deal with traffic control related problems in ATM systems because of its self-learning capability. We propose a PSD-based neural-net connection admission control (PNCAC) method for an ATM system. Under the QoS constraint, we construct a decision hyperplane of the connection admission control according to parameters of the power spectrum. We further adopt the learning/adapting capabilities of the neural network to adjust the optimum location of the boundary between these two decision spaces. Simulation results show that the PNCAC method provides a superior system utilization over the conventional CAC schemes by as much as 18%, while keeping the QoS contract.

We analyze a hierarchical cellular system with an overflow scheme and finite queues for new and handoff calls. The effect of the guard channel scheme and the effects due to reneging of waiting new calls because of the caller's impatience and dropping of queued handoff call as the callers move out of the handoff area are considered. We successfully solve the system by adopting the multi-dimensional Markovian chain and use the transition-probability matrix and the signal-flow graph to obtain the average new-call blocking probability, the forced termination probability, and the average waiting time of queued new and handoff calls. We further investigate how the guard channel scheme and buffer provision scheme affect the hierarchical cellular system. The results show that it seems unnecessary to provide a guard channel and waiting buffer for microcells in the hierarchical cellular system with an overflow mechanism.

This paper proposes a neural fuzzy connection admission control (NFCAC) scheme, which combines benefits of fuzzy logic controller and learning abilities of the neural-net, to solve the connection admission control (CAC) problems in ATM networks. Fuzzy logic systems have been successfully applied to deal with the traffic control related problems and provided a robust mathematical framework for dealing with "real-world" imprecision; multilayer neural networks are capable of producing complex decisions with arbitrarily nonlinear boundaries and they have been used as a solution for CAC. However, the application of a neural network or a fuzzy logic system to CAC presents some difficulties in a real system operation. The proposed NFCAC solves the difficulties by combining the benefits of the existing traffic control mechanisms, linguistic control strategy of the fuzzy logic controller and the learning ability of the neural net. Simulation results show that the proposed NFCAC saves a large amount of training time and simplifies the design procedure of a CAC controller but provides a superior system utilization, while keeping the QoS contract, than either the neural network or fuzzy logic system does.

This paper presents the design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for asynchronous transfer mode (ATM) networks. The fuzzy traffic controller is a fuzzy implementation of the two-threshold congestion control method and the equivalent capacity admission control method extensively studied in the literature. It is an improved, intelligent implementation that not only utilizes the mathematical formulation of classical control but also mimics the expert knowledge of traffic control. We appropriately choose input linguistic variables of the fuzzy traffic controller so that the controller is a closed-loop system with stable and robust operation. We extract knowledge of conventional control methods from numerous analytical data using a clustering technique and then use this knowledge to set parameters of the membership functions and fuzzy control rules via fuzzy set manipulation (linguistically stated but mathematically treated) with the aid of an optimization technique named genetic algorithm (GA). Simulation results show that the proposed fuzzy admission control improves system utilization by a significant 11%, while maintaining the quality of service (QoS) contract comparable with that of the conventional equivalent capacity method. The performance of the proposed fuzzy congestion control method is also 4% better than that of the conventional two-threshold congestion control method.

Due to the demand for high-speed and multimedia services, ATM (asynchronous transfer mode) networks have increasingly received high attention. The multimedia services in ATM networks have diverse traffic characteristics and service requirements, appropriate traffic controls of call admission and congestion are needed so as to guarantee the required quality of service (QOS) for services. Previous studies on the traffic control were based on the results of queueing analyses. In the present paper, an alternative traffic control for admission in conjunction with congestion based on fuzzy set theory are proposed for an ATM network. The performance evaluation via simulation shows the effectiveness of the proposed method in comparison with the equivalent capacity in Guerin et al. (1991); link utilization is effectively improved by 8% while keeping the QOS contract.>

In a cellular mobile radio system that supports various types of platforms, a platform may consist of multiple calls. A multiple-call hand-off problem exists as platforms move across a cell boundary. Some recent works have studied the multiple-call hand-off problem. We extend the earlier work by considering the system providing two separate queues for both new and hand-off calls. We analyze the system by means of continuous-time Markov chain approach. The performance measures obtained include the blocking probability of new call, and the terminating probability of hand-off call.>

We present here an exact method for analyzing the queueing behavior of packet-switched data in a new basic rate user-network interface of ISDN. The new interface of ISDN in this paper adopts statistical multiplexing for providing integrated voice and data services. In order to properly represent the arrival of multirate data service, this interface assumes that the data packet input process is in batch Poisson. Furthermore, it is modeled by multiple synchronous servers with priority queues and finite waiting room with complete rejection strategy. The analytical method, with an application of the residue theorem in complex variable, can accurately obtain the data performance measures.>

Queueing of new or handoff calls can minimize blocking probabilities or increase total carried traffic. This paper investigates a new cutoff priority cellular radio system that allows finite queueing of both new and handoff calls. We consider the reneging from the system of queued new calls due to caller impatience and the dropping of queued handoff calls by the system as they move out of a handoff area before being accomplished successfully. We use signal-flow graphs and Mason's formula to obtain the blocking probabilities of new and handoff calls and the average waiting times. Moreover, an optimal cutoff parameter and appropriate queue sizes for new and handoff calls are numerically determined so that a proposed overall blocking probability is minimized.>

This multiplexer is assumed to have wideband (WB) and narrowband (NB) customers, where the arrival process for the former is modeled as a single birth-death Markovian chain and that of the latter is modeled as an independent Poisson. Each NB customer needs a single server for service while each WB customer needs more than one server; both of their service times are the same and constant. Higher access priority is given to WB customers. To protect the NB service quality, a cutoff parameter which denotes the maximum number of WB customers simultaneously served in one slot, is provided. Performance measures including queue length distribution, blocking probability, and mean waiting time are obtained.>

This paper studies the effect of routing on the behavior of a finite queue which accepts batch Poisson inputs and receives service from multiple synchronous servers. Upon the arrival of a group of customers idle or pseudoidle servers will be considered first in routing decisions. Otherwise routing will be determined randomly in accordance with a preset probability distribution. Results obtained include state probability, blocking probability, delay, and throughput. Validity of analysis has been verified by computer simulations. These results can he used in evaluating the performance of a computer communication network.