To address the scalability challenges of quantum computing, the industry is shifting from monolithic architectures to modular quantum systems. By interconnecting multiple quantum processing units (QPUs) through communication links, modular quantum systems can scale to much higher number of qubits. However, the delay of inter-QPU operations is an order of magnitude greater than that of intra-QPU operations. To minimize the final circuit latency, a well-considered initial assignment of logical qubits in the circuit to QPUs in the system and careful insertion of inter-QPU operations are required. In this paper, we propose a timing-aware end-to-end circuit compilation framework for modular quantum systems. In the placement stage, a dependency- and interaction-aware assignment strategy is developed to assign logical qubits that interact early and frequently to nearby QPUs. In the routing stage, we optimize the insertion of inter-QPU operations and multiple intra-QPU operations to enable the execution of the gates. The selection of both inter- and intra-QPU operations is based on their circuit latency overhead, which is estimated using the operation delay and the available idle time of operand qubits that can cover the delay, as well as their benefit to subsequent gates. Our experiments assumed a realistic modular quantum system consisting of three interconnected QPUs with a total of 1,386 physical qubits. We evaluated our framework using two benchmark sets consisting of reversible arithmetic circuits and algorithmic circuits, with up to 1,300 qubits and over 800,000 two-qubit gates. Experimental results demonstrate that our approach outperformed the state-of-the-art compilation approach for modular quantum systems with over 73.8% and 46.9% reduction in final circuit latency and number of inserted inter-QPU operations, respectively. In addition, this advantage is preserved on a larger modular quantum system with four QPUs arranged in a square topology.

Advanced silicon-based spin-qubit chips are being developed by the industry because of its promising scalability for large-scale quantum computing. The silicon-based fabrication of spin-qubit devices allows them to scale to thousands of qubits while maintaining a relatively small area compared to other quantum technologies such as superconducting or neutral atom. However, the unique characteristics of spin-qubit devices limit the applicability of existing qubit routing methods developed for other quantum devices, and the state-of-the-art routing approach for spin-qubit devices overlooks some key factors, leading to suboptimal solution quality. We introduce a novel routing method for spin-qubit devices that leverages Dijkstra’s algorithm and breadth-first search to determine the routing path for the operand qubit(s) of each gate. To avoid redundant computations for routing paths, we determine the routing region for each gate and represent it as a bit vector, which enables efficient overlap checking between routing regions. We performed experiments on four benchmark sets, including circuits with up to 1617 qubits and more than 700,000 gates. These benchmark sets cover a wide range of quantum circuits, including reversible arithmetic, algorithmic, synthesized, and random circuits. Our proposed method resulted in high-quality routing solutions and outperformed the state-of-the-art qubit routing approach on spin-qubit devices with over 29% and 7% reduction in operation overhead and depth overhead, respectively.

To execute a quantum program, it has to be compiled for execution on the target quantum processor. The program is first converted into a logical circuit composed of elementary gates supported by the target processor. Most often the logical circuit cannot be executed directly on the quantum processor due to the limited connectivity between the physical qubits of the processor. So, a quantum compiler needs to perform qubit routing by inserting auxiliary gates to execute operations like SWAP, MOVE, and BRIDGE in order to satisfy the connectivity constraint. Qubit routing yields a physical circuit that can be executed on the target processor. Finally, the physical circuit still has to be scheduled considering the gate delays and the control constraints imposed by the shared classical control electronics of the quantum processor. For noisy intermediate-scale quantum processors, it is important to minimize the latency of the final scheduled physical circuit. However, solving qubit routing without considering gate delays and control constraints will inevitably lead to suboptimal final results. Here we propose a control and timing-aware qubit routing algorithm, CTQr, considering gate delays and control constraints. Moreover, CTQr performs gate merging on the fly in order to minimize the final circuit latency. The experimental results show that CTQr outperforms the state-of-art approach with 11.2%, 8.8%, and 54.6% average reduction in the circuit latency, number of additional gates, and execution time, respectively.

Traditionally, a standard cell library is composed of pre-designed cells all of which have identical height so that the cells can be placed in rows of uniform height on a chip. The desire to integrate more logic gates onto a single chip has led to a continuous reduction of row height with reduced number of routing tracks over the years. It has reached a point that not all cells can be designed with the minimum row height due to internal routability issue. Hybrid-row-height IC design with placement rows of different heights has emerged which offers a better sweet spot for performance and area optimization. [7] proposed the first row planning algorithm for hybrid-row-height design based on k-means clustering to determine the row configuration so that the cells in an initial placement can be moved to rows with matching height with as little cell displacement as possible. The biggest limitation of the k-means clustering method is that it only works for designs without any macros. Here we propose an effective and highly flexible dynamic programming approach to determine an optimized row configuration for designs with or without macros. The experimental results show that for designs without any macros, our approach resulted in 30.7% reduction in total cell displacement and 7.4% reduction in the final routed wirelength on average compared to the k-means clustering approach while satisfying the timing constraints. Additional experimental results show that our approach can comfortably handle designs with macros while satisfying the timing constraints.

In current quantum devices, physical qubits are not fully connected so that two-qubit interaction can only be performed between specific pairs of physical qubits. To execute a quantum circuit, it is necessary to transform it into a functionally equivalent one that respects the constraints imposed by the target architecture. Quantum circuit transformation inevitably introduces additional gates which reduces the fidelity of the circuit. Therefore, it is important that the transformation method completes the transformation with minimal overheads. Quantum circuit transformation consists of two steps, initial mapping and qubit routing, and here we propose a DEAR (Dynamically-Extract-and-Route) framework to solve the qubit routing problem. A key insight of DEAR is that by maintaining a sufficient lookahead ability all the time, we can make much better routing decisions while effectively controlling the runtime. In our DEAR framework, we periodically extract a subcircuit by identifying a set of remaining gates that should be executed earlier than the others, and then applies A* search to determine how to insert a sequence of additional SWAP and BRIDGE operations into the subcircuit to obtain a high quality routing solution with minimum number of additional gates. In this work, we consider the insertion of both SWAP and BRIDGE operations while most previous works deal with SWAP insertion only. We evaluate our approach with four benchmark sets with different characterization on IBM Tokyo. The experimental results show that our approach outperforms the state-of-the-art work utilizing SWAP and BRIDGE insertion with 19.1% and 77.5% average reduction in the number of additional gates and runtime, respectively, starting from the same initial mapping. We also performed experiments on larger quantum devices such as Google Sycamore and IBM Rochester both with 53 physical qubits that have largely different architectures from one another and from IBM Tokyo. Again, DEAR was much faster and required significantly less amount of additional gates.

Quantum computing is gaining more and more attention due to its huge potential and the constant progress in quantum computer development. IBM and Google have released quantum architectures with more than 50 qubits. However, in these machines, the physical qubits are not fully connected so that two-qubit interaction can only be performed between specific pairs of the physical qubits. To execute a quantum circuit, it is necessary to transform it into a functionally equivalent one that respects the constraints imposed by the target architecture. Quantum circuit transformation inevitably introduces additional gates which reduces the fidelity of the circuit. Therefore, it is important that the transformation method completes the transformation with minimal overheads. It consists of two steps, initial mapping and qubit routing. Here we propose a reinforcement learning-based model to solve the initial mapping problem. Initial mapping is formulated as sequence-to-sequence learning and self-attention network is used to extract features from a circuit. For qubit routing, a DEAR (Dynamically-Extract-and-Route) framework is proposed. The framework iteratively extracts a subcircuit and uses A* search to determine when and where to insert additional gates. It helps to preserve the lookahead ability dynamically and to provide more accurate cost estimation efficiently during A* search. The experimental results show that our RL-model generates better initial mappings than the best known algorithms with 12% fewer additional gates in the qubit routing stage. Furthermore, our DEAR-framework outperforms the state-of-the-art qubit routing approach with 8.4% and 36.3% average reduction in the number of additional gates and execution time starting from the same initial mapping.

With the existing Long Term Evolution(LTE) based Cellular Vehicle to Anything(C-V2X) infrastructure and the recent advancements in 5G implemented Ultra Reliable Low Latency Communication(URLLC), comes the need of fast and secure communications in Connected Autonomous Vehicles(CAVs). Physical layer can always be a fast and reliable medium for exploitation while having legit communication as a source for performance and latency optimization besides the enhanced spectrum efficiency depicted by multicasting and its adequate worth. Utilization of CAVs as connected IoT resources for load mitigation is also eminent from research. In this article, we benefit the 5G V2X environment of CAVs for road safety use case where a secure geo-specific locally containerized co-operative aware autonomous driving system would be the prime concern. In the localized V2X server, Roadside units(RSUs) get benefit from physical layer parameters to generate a key for end to end(E2E) communication while the On Board Unit(OBU) containers provide the cryptographic shreds as microservices in real-time as they do while monitoring or location updating. Mininet and RYU as network emulator and controller are used for the scenario implementation and analysis. Beyond RSU, no need of secure communication exists any longer due to the broadcast nature of messages for all involved vehicles in the marked premises so instead of security, we put prime focus on least latency due to highly dynamic environment and we did zero forced sparse channel estimation for evolved multimedia broadcast multicast service(eMBMS) in C-V2X.

Fog computing, the distribution of computing resources closer to the end devices along the cloud-to-things continuum, is recently emerging as an architecture for scaling of the Internet of Things (IoT) sensor networking applications. Fog computing requires novel computing program decompositions for heterogeneous hierarchical settings. To evaluate these new decompositions, we designed, developed, and instrumented a fog computing testbed that includes cloud computing and computing gateway execution points collaborating to finish complex data analytics operations. In this interactive demonstration we present one fog-specific algorithmic decomposition we recently examined and adapted for fog computing: a multi-execution point linear regression decomposition that jointly optimizes operation latency, quality, and costs. The demonstration highlights the role fog computing can play in future sensor networking architectures, and highlights some of the challenges of creating computing program decompositions for these architectures. An annotated video of the demonstration is available at [5].

In this study, random incomplete coloring (RIC) with low time-complexity and high spatial reuse is proposed to overcome in-between wireless-body-area-networks (WBAN) interference, which can cause serious throughput degradation and energy waste. Interference-avoidance scheduling of wireless networks can be modeled as a problem of graph coloring. For instance, high spatial-reuse scheduling for a dense sensor network is mapped to high spatial-reuse coloring; fast convergence scheduling for a mobile ad hoc network (MANET) is mapped to low time-complexity coloring. However, for a dense and mobile WBAN, inter-WBAN scheduling (IWS) should simultaneously satisfy both of the following requirements: 1) high spatial-reuse and 2) fast convergence, which are tradeoffs in conventional coloring. By relaxing the coloring rule, the proposed distributed coloring algorithm RIC avoids this tradeoff and satisfies both requirements. Simulation results verify that the proposed coloring algorithm effectively overcomes inter-WBAN interference and invariably supports higher system throughput in various mobile WBAN scenarios compared to conventional colorings.

Machine-to-Machine (M2M) communication has recently become a hot topic in 4G Standardization Committee, such as IEEE 802.16p and LTE-A. In an OFDMA cellular network embedded with a M2M system, we characterize system energy consumption (EC) and average delay for the M2M system and propose energy-efficient Machine Node (MN) access control schemes, jointly implementing MN grouping and coordinator selection, under a 2-hop transmission framework to minimize system EC with limited resource amount assigned by BS while satisfying the system delay requirement. In the simulation results, the tradeoff between EC and delay is investigated with respect to given resource amount, and the relationship between number of coordinators and total energy consumption is studied. Furthermore, the proposed schemes need relatively less coordinators to achieve very low EC, and can achieve near-optimal EC with any delay requirement.

Uplink energy efficient communication is an important issue due to the limited battery power for Mobile Stations (MSs). In this paper, the average 'Bits-per-Joule' is defined as the energy efficiency metric, and the optimal joint power allocation for both MS and RS is derived for maximizing MS' energy efficiency. Based on that, we developed a low-complexity subcarrier-power allocation and relay selection scheme for maximizing energy efficiency per user in an OFDMA cooperative relay network, assuming full channel state information (CSI) is available at both relay nodes and BS nodes. The numerical result demonstrates that the energy efficiency performance of cooperative relay networks can be better in the range of 15% to 30% as compared to that of single BS networks. It also shows our proposed scheme has significant improvement on the energy efficiency performance as compared with conventional max-throughput and fairness-based EPA schemes. Furthermore, the proposed scheme can achieve similar performance in much lower computational complexity as compared with the exhaustive search scheme.

In this paper, a 2-Dimension (2-D) resource allocation problem for energy efficiency maximization in the uplink of an OFDMA network is studied. Firstly, with the metric of energy efficiency, measured as bits-per-joule, we convert the complex problem of optimizing the system energy efficiency, defined as sum of users' bits-per-joule, into an optimal achievable rate comparison problem, and devise a low-complexity sub-optimal resource allocation scheme for maximizing it while satisfying each user's resource requirement. The proposed scheme implements both power allocation and Resource Unit (RU) assignment devised by exploiting the optimal achievable rate maximizing user's energy efficiency. The numerical result shows that the proposed scheme outperforms widely-used schemes with Equal Power Allocation (EPA), such as maximum SNR and Proportional Fair, in terms of performance of bits-per-joule with different resource requirement and can achieve similar results as the exhaustive search scheme in much lower complexity.

We present an optimization architecture for a cross- layer design in wireless video transmission and investigate the multicarrier effect on wireless communication. To transmit video frames constrained by delay bound and target frame error rate (FER), we formulate an optimization problem to minimize the required resource units for a single user by adapting payload length, M-ary modulation, block size, and code rate. Instead of using exhaustive approach, we propose a minimal required resource units (MRRU) algorithm with feedback of mean mutual information per bit (MMIB) to resolve this problem by binomial distribution function approximation. Finally, the performance comparison between MRRU algorithm and maximum throughput approach is given, and the effect on multicarrier system in a correlated Rayleigh fading channel is shown.

As more and more machines are networked, machine-to-machine (M2M) communication have captured attention of IEEE 802.16 and 3GPP which, however, have only described possible system architecture, features, and issues, but no specification of M2M communication so far. Therefore, it is important for us to define the problems and provide solutions. Since most of the machine devices (MD) are battery equipped, and the number of MD is expected to be significantly more than human population, there are two critical requirements in M2M communications, low uplink energy consumption (EC) and massive access management. In this paper, we propose MD access control algorithms to ensure low uplink EC of M2M communication under the constraint of maximum number of access from MD to BS by implementing grouping and coordinator selection. Moreover, weighting factor which takes channel condition between the coordinator and base station (BS) into consideration is introduced to further improve EC performance. Numerical results show that our algorithms can not only achieve lower EC but also adapt to various distribution of MD.

A novel method of using waveform retracked altimeter observations on land for the monitoring of the solid Earth deformation in Taiwan is described in the article. The southwestern coasts of Taiwan have been experiencing a significant land subsidence in the rate of ~8 em/year, due to, in part, the excess extraction of the underground water. The first-order leveling networks and the GPS control points available in the area were used to verified the results of the waveform retracked altimeter observation. In this study, we proposed to use TOPEX/Poseidon (TiP) altimeter land observations from 1992--2002 on one TIP groundtrack that passes the southwestern coasts of Taiwan where is part of the Chia-Nan Plain with the main vegetation being rice paddy. This special vegetation and flat terrain provide an opportunity for successful waveform retracking. The comparison between TiP retracked result and that from the leveling network and GPS control points are presented.

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.

In this paper, we will investigate the performance of scalable video streaming services in mobile WiMAX systems. We will show that for each user the implementation of multiple connections with feedback information of the available transmission bandwidth is critical for supporting H.264/AVC-based scalable video streaming in which the transmission packets can be further separated into multiple levels of importance. Based on a mobile WiMAX simulation platform including both the video encoder/decoder server and IEEE 802.16e MAC controls, the MAC performance evaluation and subjective test are used to quantify the visual performance between two-connection and one-connection scenarios.

To provide better QoS of scalable streaming services in mobile WiMAX systems, this paper proposes a cross-layer design between the streaming server and mobile WiMAX base station. We will show that the implementation of multiple connections with feedback information of the available transmission bandwidth is critical for supporting scalable video streaming in which the transmission packets can be further separated into multiple levels of importance. To quantify the performance, a mobile WiMAX simulation platform including both the video encoder/decoder server and IEEE 802.16e MAC controls is developed.

Video streaming is one of the killer applications for cellular communications. The MPEG-4 fine-granularity scalability video coding technique can adapt to bandwidth variation and random packet errors. In this paper, to explore the impacts of cellular channel characteristics on the tolerance of buffer performance and quality of service, a novel statistical model-based adaptive media playout (AMP) is proposed by utilizing the statistical assumptions of both arrival and departure processes for a better decision on the dynamic threshold adjustment and frame-rate adjustment. Based on third-generation cellular transmission environment, simulation results will demonstrate that as compared to other AMP schemes, the proposed AMP control provides better visual quality with lower complexity.

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.

The IEEE 802.11e introduces the Hybrid Coordination Function (HCF) to provide required mechanism for supporting real-time services. In this paper, based on the calculated transition probability of a typical adaptive auto rate fallback (AARF) algorithm, a buffer-time (BT) based call admission control is proposed. Results show that the BT CAC not only utilizes the wireless resources more efficiently, but also satisfies the crucial QoS demanding for different real-time applications like video streaming and VoIP.

Channel predictor implemented by discrete Fourier transform (DFT) is considered, which predict the channel gain of each receiving path by adaptive filters. A fuzzy logic controlled step size could overcome the drawback of conventional least-mean-square error (LMS) algorithm. In this paper, LMS algorithm with fuzzy updated step size is proposed to realize the adaptive channel prediction in orthogonal frequency division multiplexing (OFDM) systems. Simulation results show that as compared to conventional LMS algorithm, the proposed algorithm can achieve a faster convergence rate and lower steady-state mean square error. The proposed algorithm can also achieve more robust tracking ability in the various channel environments.

This paper presents the performance results of a 1xEV-DO system. With different fading channels, data rate control lengths and overhead channel power levels, the system performance will vary. While optimizing the forward-link throughput becomes critical for asymmetric traffic demand, it is important to ensure that the reverse-link throughput is sufficient to support the data traffic ratio of 4:1 to 6:1 (forward/reverse). In this paper, the forward-link throughput is simulated. The final throughput is based on field measured channels for different morphologies. On the reverse link capacity, the throughput is analyzed based on an analytical formula that considers pilot, DRC and data channels.