Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on lowcost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map through a hierarchical structure can refine exteroception processing, it introduces significant computational overhead, noise, and delays. In this paper, we present MOVE, a one-stage end-to-end learning framework capable of multi-skill omnidirectional legged locomotion with limited view in 3D environments, just like what a real animal can do. When movement aligns with the robot's line of sight, exteroceptive perception enhances locomotion, enabling extreme climbing and leaping. When vision is obstructed or the direction of movement lies outside the robot's field of view, the robot relies on proprioception for tasks like crawling and climbing stairs. We integrate all these skills into a single neural network by introducing a pseudo-siamese network structure combining supervised and contrastive learning which helps the robot infer its surroundings beyond its field of view. Experiments in both simulations and real-world scenarios demonstrate the robustness of our method, broadening the operational environments for robotics with egocentric vision.

Localisation of mobile devices has been a topic of academic research and industry practice for solving various application problems, examples can be footfall counting and profiling used for location based digital or physical advertising, crowd monitoring for public security, emergency handling, transport measurement and management, etc. Often the solutions for localisation require large scale networks hardware and/or software upgrades, which can be very costly. However, we note the fact that many commercial use cases actually do not require very high resolution of localisation and satisfactory level of accuracy may be sufficient for attaining business decision quality. A reasonable trade-off between achieving business value and minimising additional costs on network equipment purchase and maintenance is to build solutions that rely only on telco-network data and utilize data mining methods to improve the localisation accuracy of mobile devices that carried by subscribers. In this work, we aim to achieve acceptable accuracy for localisation at the resolution of region of interest (ROI), the exact shape of which is defined according to business requirements. One example is the geographical division of planning sub-zone in Singapore. We make use of the Global Positioning System (GPS) locations extracted from mobile broadband log that contain the longitude and latitude of the subscriber to annotate the telco-network data. We experimented with three learning models: maximum likelihood estimation, dominant serving ROI, and random forest, along with the baseline of localisation based on cellular tower locations. The experiment results demonstrate the effectiveness of the proposed models and demonstrate accuracy improvement from baseline of 37.8% (naive cellular tower localisation) to 78.4% (random forest classification).

Massive amount of spatio-temporal data is generated as a result of user movement and data access activities, from both smart mobile devices and network infrastructure. We aim to be able to derive mobility intelligence from spatiotemporal data at scale with efficiency and yet meet the needs of diverse applications. In this paper, we propose Mobility GenomeTM, a computational framework that enables efficient and extensible discovery of mobility intelligence from large-scale spatio-temporal data. The framework is organised as multiple layers composed of fundamental and extensible computational units. We describe several algorithms and models, such as Human Daily Activity to demonstrate the derivation of mobility intelligence from these units, together with validation results. The framework has been integrated into our Mobility Intelligence platform, processing hundreds of millions of records per day in our production environment. We also show the possibility of building more advanced applications on top of the framework such as detection of home and work location, origin-destination trips and footfall analysis. With its deployment, the framework helps us avoid duplicated efforts on repeating common data processing and algorithmic tasks while serving a diverse set of mobility intelligence applications.

This paper studies a multiple-input single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and power transfer (SWIPT), where a multi-antenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). We characterize the capacity region for ID receivers under given energy requirements for EH receivers, by solving a sequence of weighted sum-rate (WSR) maximization (WSRMax) problems subject to a maximum sum-power constraint for the AP, and a set of minimum harvested power constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance constraints (MaxLTCCs and MinLTCCs), which has not been addressed in the literature and is challenging to solve. By extending the general BC-multiple access channel (MAC) duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient algorithm to solve this problem globally optimally. Numerical results are presented to validate our proposed algorithm.

This paper studies a multiple-input–single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and power transfer, where a multiantenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). In particular, pseudorandom sequences that area prioriknown and therefore can be cancelled at each ID receiver are used as the energy signals, and the information-theoretically optimal dirty paper coding is employed for the information transmission. We characterize the capacity region for ID receivers by solving a sequence of weighted sum-rate (WSR) maximization (WSRMax) problems subject to a maximum sum-power constraint for the AP, and a set of minimum harvested power constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance constraints (MaxLTCCs and MinLTCCs), which differs from the celebrated capacity region characterization problem for MISO-BC under a set of MaxLTCCs only and is challenging to solve. By extending the general BC–multiple-access-channel duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient iterative algorithm to solve this problem globally optimally. Furthermore, we also propose two suboptimal algorithms with lower complexity by assuming that the information and energy signals are designed separately. Finally, numerical results are provided to validate our proposed algorithms.

The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs), to improve mobile data rates, has been recently proposed. However, the high density of active APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, e.g., network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this paper, we propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, i.e., group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve our problem. Leveraging on the celebrated UL-DL duality result, we show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations.

Cloud radio access network (C-RAN) has been recently proposed, in which densely deployed access points (APs) are empowered by cloud computing, to achieve enormous mobile data rates. However, close proximity of many active APs results in more severe interference and also inefficient energy consumption. To tackle this problem, we propose a joint downlink (DL) and uplink (UL) user-AP association and beamforming design in this paper to coordinate interference in the C-RAN for energy minimization. The design problem is shown to be NP hard, but exhibits an interesting “group-sparse” property. By establishing a virtual DL transmission for the original UL transmission based on the celebrated UL-DL duality result, we convert the problem to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively, and obtain an efficient solution through “group-sparse” optimization.

In this paper, we formulate and solve a weighted-sum transmitter and receiver energy minimization (WSTREMin) problem in the downlink of an orthogonal frequency division multiplexing (OFDM) based multiuser wireless system. The proposed approach offers the flexibility of assigning different levels of importance to base station (BS) and mobile terminal (MT) power consumption, with the BS being connected to the grid and the MT relying on batteries. To obtain insights into the problem, we first consider two extreme cases separately, i.e., weighted-sum receiver-side energy minimization (WSREMin) for MTs and transmitter-side energy minimization (TEMin) for the BS. It is shown that Dynamic TDMA (D-TDMA), where MTs are scheduled for single-user OFDM transmissions over orthogonal time slots, is the optimal transmission strategy for WSREMin at MTs, while OFDMA is optimal for TEMin at the BS. As a hybrid of the two extreme cases, we further propose a new multiple access scheme, i.e., Time-Slotted OFDMA (TS-OFDMA) scheme, in which MTs are grouped into orthogonal time slots with OFDMA applied to users assigned within the same slot. TS-OFDMA can be shown to include both D-TDMA and OFDMA as special cases. Numerical results confirm that the proposed schemes enable a flexible range of energy consumption tradeoffs between the BS and MTs.

Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. Cell coverage is usually pre-designed based on the estimated peak traffic load. However, traffic load in cellular networks exhibits significant fluctuations in both space and time. We design short- and long-term power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. Finally, we compare our proposed adaptation scheme with suboptimal schemes of lower complexity to demonstrate the potential energy saving in broadcast channels.

In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is assumed to be a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time ρ (dubbed the save-ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1-ρ used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the energy storage efficiency, η. We address the practical case of the secondary ESD being a battery with η <; 1, and the main ESD being a super-capacitor with η = 1. Important properties of the optimal save-ratio that minimizes outage probability are derived, from which useful design guidelines are drawn. In addition, we compare the outage performance of random power supply to that of constant power supply over the Rayleigh fading channel. The diversity order with random power is shown to be the same as that of constant power, but the performance gap can be large. Finally, we extend the proposed ST protocol to wireless networks with multiple transmitters. It is shown that the system-level outage performance is critically dependent on the number of transmitters and the optimal save-ratio for single-channel outage minimization.

Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. Cell coverage is usually pre-designed based on the estimated static (e.g. peak) traffic load. However, traffic load in cellular networks exhibits significant fluctuations in both space and time, which can be exploited, through cell range adaptation, for energy saving. In this paper, we design short- and long-term BS power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC (including BS on-off control) is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. By identifying the fact that energy saving at BS essentially comes from two major energy saving mechanisms (ESMs), i.e. range adaptation and BS on-off power control, we propose low-complexity suboptimal schemes with various combinations of the two ESMs to investigate their impacts on system energy consumption. It is shown that when the network throughput is low, BS on-off power control is the most effective ESM, while when the network throughput is higher, range adaptation becomes more effective.

In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time ρ (dubbed the save ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1 - ρ used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the ESD efficiency, η. We address the practical case of the secondary ESD being a battery with η <; 1, and the main ESD being a super-capacitor with η = 1. The optimal save-ratio that minimizes outage probability is derived, from which some useful design guidelines are drawn. Numerical results are provided to validate our proposed study.

The number of competing stations has great influence on the performance of IEEE 802.11 MAC protocol based on the distributed coordination function (DCF), which utilizes Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). Some researchers try to use performance modeling to analyze and optimize the protocol, but the strict assumptions of the modeling often lead to research results that could not be adaptive to the dynamic change of competing station number, which is extremely prevalent in today's IEEE 802.11 networks. Some other researchers try to use filters, based on accurate measurement, to estimate the number of competing stations, and improve system performance by dynamically tuning the protocol parameters. However, such mechanisms are too complex to apply in real environment. Base on our discovery, we propose a simple adaptive optimization mechanism, DOOR (Dynamic Optimization on Range), for the IEEE 802.11 DCF, which is based on the subrange of competing station number. The reason, principle and method for partitioning subranges are introduced. Moreover, the detailed system model and performance evaluation for the new mechanism are given. The elaborate numerical results show that this mechanism could achieve much higher throughput and shorter delay than the standard IEEE 802.11 DCF in almost all the different competing stations numbers.

The dynamic character of complex and variable network environment, the key problem, puzzles the corresponding protocols design for wireless LANs. However, the distributed coordination function (DCF) of IEEE 802.11 MAC protocol, which is carrier sense multiple access with collision avoidance (CSMA/CA) using constant parameters, could not perform well when network environment changes. Thus many researchers try to optimize IEEE 802.11 DCF. However early dynamic optimization mechanisms for the protocol mostly depend on measuring the number of "competing" stations accurately. The problem of them is that the algorithms are too complex to apply in reality. In our research, we find that system performance approaches optimization with the same protocol parameters, when the number of competing stations changes within a certain range dynamically. Therefore, we propose a self-adaptive optimization mechanism, DOOR (dynamic optimization on range), for the IEEE 802.11 DCF. DOOR uses filter to estimate the range of competing station number and adjusts the protocol parameters to optimize system performance effectively. The detailed analytical model and performance evaluation for the new mechanism are given. Moreover, the measurement method and parameters of filter are introduced. At last, the elaborate numerical results show that our mechanism could not only achieve much higher throughput and lower delay than the standard IEEE 802.11 DCF along with the change of competing stations, but also improve the stability of system performance based on reasonably partitioned ranges.