Delay minimization for random scheduling in centralized wireless networks

In a wireless uplink, collisions occur when two or more wireless users transmit signals at the same time over the same channel. Traditionally, when this happens, the received packets are discarded and retransmissions are required, which is a waste of power and bandwidth. The main contributions of this dissertation include a studyof approaches for collision resolution, i.e., recovery of collided packets, the design of pulse-shape functions that facilitate collision resolution and also the analysis ofpacket delays in a cellular wireless network whose base station has collision resolution capability. In the first part of this dissertation a new scheme, namely ALOHA with Collision Resolution (ALOHA-CR), is proposed, which is a cross-layer approach for highthroughput wireless communications in a cellular uplink scenario. Transmissions occur in a time-slotted ALOHA-type fashion but with an important difference: simultaneous transmissions of two users can be successful. When two users transmit, the collision is resolved by oversampling the collision signal and exploiting independent information about the users that is contained in the signal polyphase components. The performance of ALOHA-CR is demonstrated on the Wireless Open Access Research Platform (WARP) testbed containing five software defined radio (SDR) nodes. The testbed results indicate that ALOHA-CR leads to significant increase in throughput and reduction of service delays as compared to ALOHA. The second part of this dissertation focuses on optimal pulse-shape design for collision resolution. As mentioned above, the collided packets are separated by oversampling the collision signal. Because of oversampling, high correlations can occur between the columns of the virtual multiple-input multiple-output(MIMO) system matrix, which can be detrimental to user separation. A novel pulse-shape waveform design is proposed, which results in low correlation between the columns of the system matrix, while it exploits all available bandwidth as dictated by a spectral mask. In the third part, we study the delay properties of random scheduling (RS) in a cellular wireless network, under the assumption that the base station (BS) has multi-packet reception (MPR) capability. We minimize the expected delay of RS by determining the scheduling probabilities of nodes that will transmit simultaneously. For the perfect reception case, (i.e., when the success probability of transmissions is 1), a lower bound of the delay performance for an arbitrary scheduling policy is provided. The imperfect reception case is also studied and a convex optimization formulation is proposed, which can minimize theupper bound on the expected delay of RS. An approximation and a lower bound on the expected delay of RS are also developed under the assumption that the basestation can support simultaneous transmission of two users.

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we consider the analysis and optimization of caching and multicasting in a large-scale cache-enabled wireless network. We propose a random caching and multicasting scheme with a design parameter. By carefully handling different types of interferers and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general region, utilizing tools from stochastic geometry. We also obtain a closed-form expression for the successful transmission probability in the high signal-to-noise ratio (SNR) and user density region. Then, we consider the successful transmission probability maximization, which is a very complex non-convex problem in general. Using optimization techniques, we develop an iterative numerical algorithm to obtain a local optimal caching and multicasting design in the general region. To reduce complexity and maintain superior performance, we also derive an asymptotically optimal caching and multicasting design in the asymptotic region, based on a two-step optimization framework. Finally, numerical simulations show that the asymptotically optimal design achieves a significant gain in successful transmission probability over some baseline schemes in the general region.

Different network applications may have different delay requirements. How to evaluate the delay performance numerically is important for network design. Although the delay performances of random scheduling (RS) and round robin (RR) are known in literature, the accurate delay expression of first-input first-output (FIFO) scheduling remains to be discovered. In this paper, by combining stochastic geometry and queueing theory, we derive the accurate and tractable mean delay expression for FIFO, which accounts for the impact of random traffic arrivals and queuing interactions. The coupling relation between the success transmission probability and the network active probability is captured. Based on this, we further derive the mean packet delay of the whole network. The simulation verifies the accuracy of the analytical expressions. We find that for light traffic, it is best to choose FIFO from the delay aspect, and for heavy traffic, choosing RR is best. We also obtain the critical packet arrival rate for different scheduling selections.

With the rapid proliferation of broadband wireless services, it is of paramount importance to understand how fast data can be sent through a wireless local area network (WLAN). Thanks to a large body of research following the seminal work of Bianchi, WLAN throughput under saturated traffic condition has been well understood. By contrast, prior investigations on throughput performance under unsaturated traffic condition was largely based on phenomenological observations, which lead to a common misconception that WLAN can support a traffic load as high as saturation throughput, if not higher, under nonsaturation condition. In this paper, we show through rigorous analysis that this misconception may result in unacceptable quality of service: mean packet delay and delay jitter may approach infinity even when the traffic load is far below the saturation throughput. Hence, saturation throughput is not a sound measure of WLAN capacity under nonsaturation condition. To bridge the gap, we define safe-bounded-mean-delay (SBMD) throughput and safe-bounded-delay-jitter (SBDJ) throughput that reflect the actual network capacity users can enjoy when they require finite mean delay and delay jitter, respectively. Our earlier work proved that in a WLAN with multi-packet reception (MPR) capability, saturation throughput scales superlinearly with the MPR capability of the network. This paper extends the investigation to the nonsaturation case and shows that superlinear scaling also holds for SBMD and SBDJ throughputs. Our results here complete the demonstration of MPR as a powerful capacity-enhancement technique for WLAN under both saturation and nonsaturation conditions.

This paper revisits the study of wireless carrier-sense multiple access (CSMA) protocols enabled with multi-packet reception (MPR) capabilities. This study employs a new paradigm in the literature of random access based on multi-objective and financial portfolio optimization tools. Under this new optimization framework, each packet transmission is regarded not only as a network resource, but also as a financial asset with different values of return and risk (or variance of the return). The objective of this network-financial optimization is to find the transmission policy that simultaneously optimizes network metrics (such as throughput and efficient power consumption), as well as economic metrics (such as fairness, return and risk). Two transmission models are considered for performance evaluation: a Bernoulli transmission model that facilitates analytic derivations, and a Markov model that considers the backlog states of the network and that facilitates dynamic stability analysis. This work is focused on the characterization of the boundary (envelope) or the Pareto optimal frontier of different types of trade-off performance region. These regions include the conventional throughput and stability regions, as well as new trade-off regions such as sum-throughput vs. fairness, sum-throughput vs. power consumption, and return vs. risk. Fairness is evaluated by means of the Gini-index, which is used in the field of economics to measure population income inequality. Transmit power is directly linked to the global transmission attempt rate. In scenarios with weak MPR capabilities, the system has problems in achieving simultaneously good values of fairness and high values of sum-throughput. This is because of an underlying non-convex throughput region which is typical of protocols dominated by unresolvable collisions. On the contrary, in scenarios with strong MPR capabilities, good fairness, higher energy consumption efficiency, and high sum-throughput performances can be simultaneously achieved. Carrier-sensing is shown to improve the convexity of the throughput region in scenarios with weak MPR, thereby achieving a better trade-off between metrics, including return and risk. However, the effects of carrier-sensing are shown to disappear in scenarios with strong MPR capabilities or with underlying convex throughput regions. The combination of MPR with carrier-sensing tools helps in reducing risk in the network and to fight issues of wireless random access such as the hidden/exposed terminal problems.KeywordsS-ALOHARandom accessMulti-objective portfolio optimizationPareto optimal trade-off curve

Wirelessly powered backscatter communication (WP-BackCom) enables battery-free Internet of Things (IoT) nodes to passively modulate and backscatter information on the energy signals transmitted by power beacons (PBs) and to harvest energy for realizing energy self-sustainability. It has been deemed a promising solution for the large-scale deployment of IoT nodes. In this paper, we exploit stochastic geometry (SG) to develop a tractable framework to evaluate the successful transmission probability in WP-BackCom while considering the imperfect successive interference cancellation (SIC) at each gateway (GW) and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU). The successful transmission probability is defined as the probability for the event that not only the BU can harvest sufficient power from PBs for sustaining its operation but also the backscattered information can be successfully decoded by the GW. We derive the active probability into a closed form and then obtain the successful transmission probability based on the random spectrum access (RSA) policy. The closed-form expression for the successful transmission probability is derived under three special cases. We also devise a closed-form expression for the sub-optimal reflection coefficient to obtain a sub-optimal successful transmission probability. The analysis reveals that the sub-optimal reflection coefficient increases with the SIC coefficient. Numerical simulations validate the correctness of the above theoretical derivations and examine the impacts of various parameters on the successful transmission probability.

In this paper, the resource allocation issues in Device-to-Device (D2D) aided heterogeneous networks are investigated, with successive interference cancellation (SIC) capabilities enabled in base stations (BSs). The problem of maximizing the sum throughput of whole network by considering real-time transmissions is formulated. After that, the globally optimal power control is carried out relying on both the enumeration and Kuhn-Munkres methods. To both guarantee the successful transmission probability (STP) of cellular users (CUEs) and reduce the system’s management overhead, a range-difference based resource allocation scheme is proposed. Numerical results show that the average throughput of CUEs can be substantially improved without sacrificing the performance of CUEs by invoking the proposed resource allocation scheme.

With the improvement of quality of service requirement and the increase of energy expenditure, integrating caching and energy harvesting (EH) to device-to-device (D2D) communication is a prospective but challenging research. In this paper, we focus on investigating the network performance with stochastic geometry for cache- and EH-enabled D2D cellular network, in which each mobile user (MU) can cache contents from the base station and be charged through EH. Specifically, taking into account the knowledge of the energy states and the requested contents, along with the randomness of content caching, each MU can fetch its requested file via three modes, i.e., self-satisfied, D2D communication and cellular downlink communication. In view of this observation, stochastic geometry is applied to analyze the system transmission model, and from which the content delivery strategies are designed, as well as the cache hit probability and successful transmission probability are characterized under three modes, respectively. Based on the analyzed results, two probabilistic caching strategies with different objectives are further designed to maximize the cache hit probability and the network successful offloading probability. Finally, comprehensive simulations are provided to analyze the effect of different parameters on network performance and verify the superiority of the proposed caching strategies.

The efficient data dissemination in an infrastructure-less environment as MANET is the challenging issues. This paper proposes to adopt the CDN Technique for disseminating information in MANET. The source node disseminates data to surrounding nodes by repeatedly transmitting batches of packets. Our proposed is developed based on the OLSR. The CDN nodes are selected from MPRs to optimally cover all subscribers and to avoid the interference problem. The packets are transmitted to destination by using broadcasting technique. The performance is evaluated as probability of successful transmission by simulation using NS3, and compared with the pure OLSR and clustering-based data transmission algorithm in VANET. Our proposed improves the probability of successful transmission and achieves higher rate at high nodes' density when comparing with pure OLSR and clustering-based algorithm respectively. The closed-form mathematical expression of the probability of successful transmission of our proposed algorithm in multi-hop network environment is derived and verified.

This paper proposes an agglomerating radio resource control method for wireless packet access systems that makes effective use of limited radio resources to provide users with their desired bandwidths. In the proposed method, each mobile station (MS) communicates with multiple base stations (BSs) simultaneously, and gathers from the neighboring BSs, as well as from the BS currently communicating with the MS, information about radio resources that have not been allocated to other MSs. The MS then sums up the bandwidths supported by the vacant resources, and if the total bandwidth is found to be larger than the MS's requirement, it keeps the connections with the multiple BSs to ensure communication continuity. This method is especially effective when traffic load is heavy in the cell hosting the mobile user. This paper evaluates the performance of the proposed method in various aspects such as throughput efficiency, resource allocation and successful packet transmission probabilities, as well as resource utilization ratio, under interference-limited environments.

With development of web application, humans have much more requirement on network Quality of Service (QoS). This paper proposes an path selection decision-making model used in application layer for Multipath Transmission (MPT) to overcome the challenge which QoS of network layer will be corrupted in multiple paths transmission mechanism. P2FT method is proposed in this paper. That module can decide path selections according to characteristics of application layer. We dynamically provide data that should transmits in each path. Further, due to characteristics of multiple paths transmission, the model macroscopically regulates the application layer to choice more appropriate path real time. In performance evaluation part, we obtain transmission time by calculating in this simulation. The simulation results from the graphs illustrate that our proposed model decrease 61.627% of transmission time and achieve 1.0000 probability of successful transmission respectively compared with traditional bearer network. The path to cooperate with each other, to achieve network resources optimization, high efficiency, high speed transmission, and make the transmission have QoS guarantee.

Collisions between vehicles and pedestrians usually result in the fatality to the vulnerable road users (VRUs). Thus, new technologies are needed to be developed for protecting the VRUs. Based on the high density of pedestrians and limited computing capability of base stations, in this paper the cloud computing technologies are adopted to handle the huge amounts of safety-critical messages. Moreover, the wireless multi-hop backhaul technology is adopted to overcome the bottlenecks of limited transmission capability and queueing delay of the transmitted safety-critical messages between base stations and clouds. Based on the multi-hop wireless transmission scheme, the signal transmission success probability and delay between pedestrians and clouds are derived for performance analysis. Furthermore, numerical simulations are performed to illustrate the relationship between the transmission success probability and the received signal to interference plus noise ratio (SINR) threshold.

Recently, research attempts have been made to jointly model spatial and temporal randomness in cellular networks. However, realistic modeling and analysis of dynamic queuing interactions in heterogeneous networks (HetNets) still need to be investigated. In this letter, we establish a realistic spatiotemporal model for a ${K}$ -tier HetNet. Particularly, we model the locations of base stations (BSs) from each tier as an independent Poisson point process (PPP). Packet arrivals are modeled as independent Bernoulli processes. To reveal actual scenario, the effect of bias association is considered. We also allow users to have various packet arrival rates to account for different traffic loads. We capture the coupling relations between transmission success probability and cell active probability. Accurate expression for the mean packet throughput of the network is derived. Our analytical results provide the way to design optimal bias association factors to maximize the mean packet throughput of the HetNet, and also help design BS densities for certain user density in order to achieve a given mean packet throughput level.

On the successful transmission probability of cooperative cognitive radio ad hoc networks

To improve signal-to-interference ratio (SIR) and make better use of file diversity provided by random caching, we consider the maximal ratio combining (MRC) receiver at each user in a large-scale cache- enabled single-input multi-output (SIMO) network. First, by utilizing tools from stochastic geometry, we derive a tractable expression and closed-form upper and lower bounds for the successful transmission probability in the general SIR threshold regime. We also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. The analytical results reveal that the successful transmission probability increases with the number of receive antennas M at each user in the general SIR threshold regime, the coefficient of the asymptotic outage probability decreases with M and the order gain of the asymptotic outage probability does not depend on M. Then, we consider the successful transmission probability maximization. In the general SIR threshold regime, we consider the maximization of the tight upper bound on the successful transmission probability by optimizing the caching distribution, which is a non-convex problem. We obtain a stationary point, by solving an equivalent difference of convex (DC) programming problem using concave-convex procedure (CCCP). We also obtain a closed-form asymptotically optimal solution in the low SIR threshold regime. The optimization results indicate that files of higher popularity get more storage resources, and the caching distribution becomes more flat when M is larger.