Base Station Schedules Research Articles

Optimal Base Station Scheduling for Device-to-Device Communication Underlaying Cellular Networks

With the emergence of demands for local area services, device-to-device (D2D) communication is proposed as a vital technology component for the next generation cellular communication to increase spectral efficiency and enhance system capacity. In a D2D underlaying cellular system, through carefully coordinated dynamic scheduling, some base stations can be shut down and the corresponding load can be transferred to the D2D communication, which provides significant energy savings without much impact on the overall system performance. In this paper, we study the base station scheduling in D2D communication given a large scale of users. By formulating this problem into a flow maximization problem that optimizes the data transmission from the base stations to the users, we obtain the optimal base station scheduling solution. Through extensive simulations with real human mobility traces, we demonstrate the effectiveness of our proposed scheme, which significantly enhances the system throughput compared with existing strategies.

Radio-aware resource allocation architecture for QoS differentiation in WiMAX networks

One of the keys to the success of WiMAX is its capability of supporting traffic with heterogeneous quality of service (QoS) requirements. The current standard specifies the general QoS architecture for WiMAX but the design of the main building blocks (e.g., connection admission control, packet schedulers) is left up to the vendors' implementation. This gap motivates the paper that aims to design and analyse a channel-aware QoS framework for efficiently managing radio resources in a WiMAX network. The proposed architecture integrates the specifications of (1) a connection admission control module and a packet scheduler implemented at the base station (BS) to manage bandwidth requests from the subscriber stations (SSs), and (2) a scheduler at the SS that distributes the granted resources among the active data flows. Simulation results show that the proposed framework enables QoS differentiation and intra-class fairness, and demonstrate that a combined design of the BS and SS schedulers is necessary for effective QoS provisioning.

Fairness Consideration of Scheduling for Real-Time Services in 4G Systems

In order to achieve higher resource utilization, the call admission control (CAC) will allow the number of connections into the system more than the system capacity can offer. However, the fairness problem will occur when these connections are real-time services. To solve this problem, in this paper, we propose a fair scheduling algorithm named contribution-based scheduling algorithm (CSA) for real-time polling service (rtPS) or guarantee bit rate (GBR) in the uplink direction in the forth-generation (4G) systems. In CSA, a mobile subscriber (MS) gains its contribution credit value at the end of each transmission time interval (TTI), which is based on the contribution of MS to the radio resource scheduling. The base station (BS) schedules the bandwidth according to the credit value. Simulation results show that CSA achieves higher fairness in bandwidth allocation while connection drop rate and queuing delay are also guaranteed as compared to the roundrobin (RR) and early deadline first (EDF) mechanisms.

Increasing Opportunistic Gain in Small Cells Through Energy-Aware User Cooperation

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. The number of users in each cell is thus reduced, which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose a user-initiated base station (BS)-transparent traffic spreading approach that leverages user-user communication to increase BS scheduling flexibility. The proposed scheme can increase opportunistic gain and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case.

Two-Stage Semi-Distributed Resource Management for Device-to-Device Communication in Cellular Networks

In cellular networks, the device-to-device (D2D) communication increases the network capacity by spatial reuse of radio resources and prolongs the battery life of devices by reducing the transmission power. In this paper, we propose a two-stage semi-distributed resource management framework for the D2D communication. At the first stage of the framework, the base station (BS) allocates resource blocks (RBs) to BS-to-user device (B2D) links and D2D links, in a centralized manner. At the second stage, the BS schedules the transmission using the RBs allocated to B2D links, while the primary user device of each D2D link carries out link adaptation on the RBs allocated to the D2D link, in a distributed fashion. The proposed framework has the advantages of both centralized and distributed design approaches, i.e., high network capacity and low control/computational overhead, respectively. We formulate the problems of RB allocation to maximize the radio resources efficiency, taking account of two different policies on the spatial reuse of RBs. To solve these problems, we suggest a greedy algorithm and a column generation-based algorithm. By simulation, it is shown that the proposed scheme achieves the near-optimal performance while reducing the control/computational overhead.

Quality of Information and Energy Efficiency Optimization for Sensor Networks via Adaptive Sensing and Transmitting

Co-optimizing information quality and energy efficiency are an important but challenging problem in sensor networks, because of the interdependency that exists between them. For example, increasing sensor sampling rate will improve information quality but cost energy consumption, due to more traffic needed to be transmitted. To address this co-optimization issue, this paper first presents a novel quality/energy efficient metric, which models the relationship of sensing, processing, and transmitting with quality and energy. Then, based on the metrics, a quality-energy adapting system is developed to exploit base station scheduling priority and techniques such as batch processing and adaptive sampling to optimize both energy efficiency and overall quality. Our results have demonstrated the usefulness of this model and its feasibility for base station to runtime co-optimize both quality and energy under changing environment and network conditions.

Two-Stage Channel Quantization for Scheduling and Beamforming in Network MIMO Systems: Feedback Design and Scaling Laws

This paper proposes an efficient two-stage channel quantization and feedback scheme for the downlink limited-feedback network multiple-input multiple-output (MIMO) system. In the first stage, the users report their best set of base-station antenna and physical resource block combinations, and the base-stations schedule the best user for each antenna in each resource block. The scheduled users are then polled in the second stage to feedback their quantized channel vectors. This paper proposes an analytical framework to show that, under a total feedback budget of B bits, the number of bits assigned to the second feedback stage should scale as log B, and in quantizing channel vectors from different base-stations, each user should allocate feedback bits in proportion to the channel magnitudes in dB scale. Under these optimized bit allocations, the overall sum rate of the system is shown to scale double-logarithmically with B, linearly with the total number of antennas, and logarithmically with transmit power, thus achieving both multiuser diversity and spatial multiplexing gains under limited feedback. Finally, realistic wireless propagation model of an urban small-cell deployment is used to show that the proposed scheme can approach the performance of a network MIMO system with full channel state information with only modest amount of channel feedback.

Opportunistic Two-Stage Feedback and Scheduling for MIMO Downlink Systems

We propose an opportunistic two-stage feedback and scheduling algorithm that is based upon zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) to reduce its feedback load. In an SUS algorithm, a base station schedules semi-orthogonal users using the feedback of all users' channel information. However, such feedback overhead significantly increases with the number of users. To reduce the feedback load of ZFBF-SUS systems, the proposed two-stage feedback scheme opportunistically and separately exploits the signal-to-interference-plus-noise ratio and orthogonality in each stage such that only a fraction of users feed back their channel information. Based on this opportunistic feedback scheme, we decouple the entire process of the SUS into two sub-processes. The proposed two-stage feedback and scheduling scheme effectively exclude the inappropriate users for ZFBF transmission in both feedback and scheduling periods, and thus save the scarce resources consumed for the feedback process. We derive an analytical expression for the average number of feedback bits of the proposed system and optimize it in an averaged sense. Further, the sum-rate of the proposed system is theoretically investigated. Both analytical and simulation results show that the proposed algorithm achieves the performance of conventional ZFBF-SUS systems with a significantly reduced number of feedback bits.

Resource allocation in relay enhanced LTE-Advanced networks

Relay deployment in future mobile networks is a vital measure to enhance the coverage region of regular base stations, to overcome shadowing dips, and to bring improvements in the cell-edge performance. In this regard, resource allocation in a relay-enhanced scenario is a key design task, and has become a very interesting research topic over the past few years. In this article, we study two main resource allocation aspects of a relay-enhanced scenario. First, we concentrate on the problem of multiplexing the relay backhaul link and the direct link at the base station scheduler for in-band as well as out-band relay operations. We propose three distinct resource partitioning strategies, and evaluate their performance via long-term evolution (LTE) system level simulations in the downlink direction. We observe that even the low implementation effort algorithms bring appreciable improvement for the cell-edge users with or without small loss in performance for the other users, thereby enhancing system fairness. Second, we visit the problem of supporting heterogeneous quality of service (QoS) requirements in a multi-user relay-enhanced network. To this end, we introduce a QoS-aware scheduler which uses packet latency and rate requirements to prioritise the scheduling decisions. Moreover, we also propose a mechanism to support QoS-constrained services to the relayed users, served over two (or more) hops. Employing an LTE system level simulator, for relay-enhanced scenario with a traffic mix having distinct QoS requirements, we demonstrate that the proposed QoS-aware resource allocation strategy significantly increases the fraction of QoS-satisfied users.

Structural Properties of Optimal Scheduling Policies for Wireless Data Transmission

We analyze a cell with a fixed number of users in a time period network. The base station schedules to serve at most one user in a given time period based on information about the available data rates and other parameter(s) for all the users in the cell. We consider infinitely backlogged queues and model the system as a Markov Decision Process (MDP) and prove the monotonicity of the optimal policy with respect to the starvation age and the available data rate. For this, we consider both the discounted as well as the long-run average criterion. The proofs of the monotonicity properties serve as good illustrations of analyzing MDPs with respect to their optimal solutions.

Effect of Spectrum Sensing Reliability on the Capacity of Multiuser Uplink Cognitive Radio Systems

In this paper, we investigate the performance of opportunistic scheduling in uplink cognitive radio (CR) systems by taking into account the CR users' spectrum sensing reliability. In uplink CR systems, each secondary transmitter (CR user) has a spectrum sensing responsibility to protect the primary system, and the secondary receiver (base station) schedules the transmission opportunities for the CR users. We propose novel optimal and suboptimal scheduling schemes by simultaneously taking into account the spectrum sensing reliability and the data channel quality. Analytical performance results for the proposed suboptimal scheduling schemes show that the spectrum sensing reliability should be considered for scheduling to maximize the capacity of the secondary system. Moreover, we also analyze the achievable multiuser diversity (MUD) gain of one of the proposed suboptimal scheduling schemes, which is based on the linear combination of the sensing channel and the data channel qualities. We show that the MUD gain of uplink CR systems grows significantly slower than that of conventional multiuser systems, particularly if the quality of the sensing channel is poor. Analytical and simulation results confirm that the proposed optimal and suboptimal scheduling schemes taking into account the spectrum sensing reliability and the data channel quality yield significant performance gains compared with conventional opportunistic scheduling.

Handoff Optimization Using Hidden Markov Model

This letter establishes the similarity between the sensor scheduling problem and the handoff (i.e., base station assignment) problem in cellular networks. A mobile user behavior is then modelled by a Hidden Markov Model (HMM). The handoff problem is formulated as an optimization problem of base station scheduling that minimizes a cost function that involves the HMM state estimation error and base station measurement costs. The optimization problem can be solved using algorithms known as partially observed Markov decision processes.

Game theory and time utility functions for a radio aware scheduling algorithm for WiMAX networks

In WiMAX systems the Base Station scheduler plays a key role as it controls the sharing of the radio resources among the users. The goal of the scheduler is multiple: achieve fair usage of the resources, satisfy the QoS requirements of the users, maximize goodput, and minimize power consumption, and at the same time ensuring feasible algorithm complexity and system scalability. Since most of these goals are contrasting, scheduler designers usually focus their attention on optimizing one aspect only. In this scenario, we propose a scheduling algorithm (called $$\mathrm{GTS_N}$$ ) whose goal is to contemporaneously achieve efficiency and fairness, while also taking into account the QoS requirements and the channel state. $$\mathrm{GTS_N}$$ exploits the properties of Time Utility Functions (TUFs) and Game Theory. Simulations prove that the performance of $$\mathrm{GTS_N},$$ when compared to that of several well-known schedulers, is remarkable. $$\mathrm{GTS_N}$$ provides the best compromise between the two contrasting objectives of fairness and efficiency, while QoS requirements are in most cases guaranteed. However, the exponential complexity introduced by the game theory technique makes it rather impractical and not computationally scalable for a large number of users. Thus we developed a suboptimal version, named sub- $$\mathrm{GTS_N}.$$ We show that this version retains most of the features and performance figures of its brother, but its complexity is linear with the number of users.

Throughput Gain Using Threshold-Based Multiuser Scheduling in WiMAX OFDMA

This paper presents the analysis of the throughput enhancement possible using threshold-based multiuser scheduling in WiMAX OFDMA. We consider a point-to-multipoint (PMP) WiMAX network where base station (BS) schedules downlink packets for simultaneous transmissions to multiple users using the WiMAX OFDMA system. WiMAX OFDMA standard specifies several subcarrier permutation options, such as the partial usage of subcarriers (PUSC), full usage of subcarrier (FUSC), and the band adaptive modulation and coding (band-AMC) among others, for mapping the physical subcarriers into logical subchannels assigned to users by the BS schedulers. In this paper, we propose the use of threshold testing prior to the process of subchannel assignment to users by the BS scheduler, as a means of throughput enhancement. In the proposed threshold-based multiuser scheduling scheme, the BS scheduler selects at any time instant users whose channel gains in the available subchannels equal or exceed a predetermined energy threshold. Thus, only users who can maximize BS throughput on the available subchannels are assigned data transmission opportunities which enhance the BS data rate, albeit at the expense of fairness to users. We quantify the throughput enhancement of the proposed system and illustrate its benefits by numerical simulations.

Performance Analysis of Joint Opportunistic Scheduling and Receiver Design for MIMO-SDMA Downlink Systems

In this work, the sum-rate performance of joint opportunistic scheduling and receiver design (JOSRD) is analyzed for multiuser multiple-input-multiple output (MIMO) space-division multiple access (SDMA) downlink systems. In particular, we study linear rake receivers with selective combining, maximum ratio combining and optimal combining in which signals received from all antennas of each mobile terminal (MT) are linearly combined to improve the effective signal-to-interference-plus-noise ratios (SINRs). By exploiting limited feedback on the effective SINRs, the base station (BS) schedules simultaneous data transmission on multiple beams to the MTs with the largest effective SINRs. Using extreme value theory, the average sum-rates and their scaling laws for JOSRD are derived. In particular, it is shown that the limiting distribution of the effective signal-to-interference (SIR) is of the Frechet-type whereas that of the effective SINR converges to the Gumbel-type. Furthermore, the SIR-based sum-rate scaling laws are found to follow ε log K with 0<;ε<;1, which stands in contrast to the SINR-based scaling laws governed by the conventional log log K form. Both analytical and simulation results confirm that significant performance improvement can be achieved by incorporating low-complexity linear combining techniques into the design of scheduling schemes in MIMO-SDMA downlink systems.

Adaptive Opportunistic Transmission in MU-MIMO Downlink with Reduced Feedback

In this paper we proposed two threshold-aided adaptive opportunistic transmission strategies in multiuser multiple-input multiple-output (MU-MIMO) downlink with limited feedback. Threshold scheme in company with transmission mode adaptation are employed to handle feedback cost and improve system performance. In one time slot, each mobile station (MS) carries out channel estimation, selects appropriate transmission mode and feeds back adaptively. With feedback information the base station (BS) schedules one user out of candidate MSs and transmits to it. One of the proposed strategies employs single-level threshold. However, proper design of the threshold is difficult. Since a tight one would result in high outage probability whereas a loose one could not reduce feedback load effectively. Thus the other method employs double-level threshold. In this scheme, the primary threshold is used for feedback reduction, the secondary one is employed to guarantee the outage performance. In evaluating the strategies, statistical analysis and Monte-Carlo simulation are used. Results show that with properly designed thresholds, the proposed schemes can greatly reduce the feedback load, achieve high throughput and good outage performance.

A Comparison of Scheduling Strategies for MIMO Broadcast Channel with Limited Feedback on OFDM Systems

We consider a multiuser downlink transmission from a base station with multiple antennas (MIMO) to mobile terminals (users) with a single antenna, using orthogonal frequency division multiplexing (OFDM). Channel conditions are reported by a feedback from users with limited rate, and the base station schedules transmissions and beamforms signals to users. We show that an important set of schedulers using a general utility function can be reduced to a scheduler maximizing the weighted sum rate of the system. For this case we then focus on scheduling methods with many users and OFDM subcarriers. Various scheduling strategies are compared in terms of achieved throughput and computational complexity and a good tradeoff is identified in greedy and semiorthogonal user selection algorithms. In the greedy selection algorithm, users are selected one by one as long as the throughput increases, while in the semiorthogonal approach users are selected based on the channel correlation. An extension of these approaches from a flat-fading channel to OFDM is considered and simplifications that may be useful for a large number of subcarriers are presented. Results are reported for a typical cellular transmission of the long-term evolution (LTE) of 3GPP.

Performance Analysis of Quality of Service in PMP Mode WiMax Networks

IEEE 802.16 standard supports point-to-multipoint (PMP) and Mesh topologies. This paper proposes a quality of service (QoS) mechanism for IEEE 802.16 in PMP mode and a base station (BS) scheduler for PMP mode. It also describes the QoS over WiMAX networks. The average WiMAX delay, load, and throughput at the BS were analyzed and compared by applying different schedulers at the BS and fixed nodes.

Asymptotic BER Analysis of a SIMO Multiuser Diversity System

Multiuser diversity is a form of diversity that is inherent in wireless systems with more than one user. For single-best user selection, it becomes a scheme whereby the base station schedules transmission to and from the user with the best channel. Traditionally, this form of diversity is considered in conjunction with adaptive modulation, where the rate of transmission is adapted to the channel quality of the best user. In this paper, we consider a fixed modulation and analyze the asymptotic bit-error-rate performance for uplink systems for a large number of users using the theory of asymptotic order statistics. Moreover, we consider multiple antennas at the base station and show that antenna correlation, perhaps surprisingly, improves the bit error rate (BER) for a large number of users. Simulation results corroborate our analytical results.

Opportunistic Feedback With a Reservation Channel in a Wireless TDD System

A random-access-based feedback protocol with a reservation-based (RB) channel is proposed for multiuser diversity in a wireless time-division-duplex system. The feedback channels consist of one RB channel and several contention-based (CB) channels. The users randomly access the CB channels if their channel states exceed a threshold, and a base station (BS) schedules a user who reports the highest signal-to-noise ratio from multiple users that successfully provide feedback. If there is no successful feedback in the CB channels, the BS uses the feedback information that a designated user reports through the RB channel. The proposed feedback protocol achieves an almost ideal sum-rate capacity with a fixed number of feedback channels regardless of the number of users.