Clients in federated learning (FL) may exhibit varying computing capabilities, leading to prolonged training latency when deploying complex deep neural networks. To address this challenge, split federated learning (SFL) presents an approach that offloads the main computational workload from resource-constrained devices to a server, while enabling parallel training. However, there are two significant limitations of existing SFL frameworks: The adoption of a uniform cut layer strategy fails to take into account the heterogeneous among clients; it fails to effectively utilize server-side resources to improve training efficiency. This article presents a framework, i.e., heterogeneous split federated learning, which considers personalized cut layer selection and server resource configuration to accelerate SFL in heterogeneous edge computing environments. By splitting the global model into two components for each client, our framework jointly optimizes both client-side workload, batch size control, and server resource configuration strategy, while considering device heterogeneity. Specifically, we develop an alternating iterative scheduling algorithm to obtain an approximate scheme for the cut layer, batch sizes, and server resource configuration to alleviate the impact of device heterogeneity. The experimental results illustrate that HSFL outperforms the compared methods, achieving performance improvements of up to 3.9%$\sim$32.2% across two datasets under various data distribution scenarios, which demonstrates the effectiveness of the proposed strategies.

LoRa, a leading wireless technology for Low Power Wide Area Networks (LPWAN), is well-known for its long transmission range and low power consumption. The extended range is primarily attributed to the Chirp Spread Spectrum technique. However, the LoRa physical layer (LoRa PHY) contributes only marginally to this advantage, as it employs an inefficient Forward Error Correction (FEC) strategy for error recovery. In this paper, we introduce Sym-FEC, a symbol-level FEC decoder designed to link the received signals' spectrum with the coding correlations inherent in LoRa PHY, thereby enhancing error recovery. The key enabler of Sym-FEC is signal copy retrieval. We begin by facilitating signal copy conversion between two symbols and extend this to the general case, where signal copy conversions can be performed between any symbols in a coding block. Approaches are also introduced to assess the validity of the block-wide decoding results. Extensive hardware evaluations demonstrate that Sym-FEC provides Signal-to-Noise-Ratio (SNR) improvement of 2.3dB to 3dB compared to the traditional decoder in LoRa PHY. Sym-FEC requires no modifications at the transmitter while incurs low storage and computational complexity at the gateway, thus can be easily integrated into gateway nodes.

Accurate long-term traffic forecasting improves traffic efficiency and safety in Intelligent Transportation Systems (ITS). However, previous methods typically overlook the complex mixed characteristics. So they struggle to capture the intricate features of newly added data effectively under distribution shifts, which exacerbates the complexity of spatial-temporal variations. Moreover, these methods fail to model global and local dynamic spatial correlations effectively, efficiently, and comprehensively. To mitigate these issues, this paper proposes an innovative Spatial-Temporal Wavelet Attention Network (STWAN). STWAN first decomposes traffic data into stable trends and fluctuating events, effectively dealing with the adverse effects of distribution shifts. Then, spatial-temporal encoder captures component-specific temporal variations and extracts dynamic global-local spatial correlations comprehensively with linear computational complexity. Additionally, transformer attention and forecasting decoder model the latent patterns, while trend-event fusion module integrates essential information for accurate forecasts. Comprehensive experiments across two real-world traffic forecasting tasks indicate that STWAN significantly outperforms state-of-the-art methods in terms of accuracy and robustness, attaining a maximum reduction of 5.54% in MAE and showcasing its robustness and broad applicability in long-term forecasting.

Low Earth Orbit (LEO) satellites have emerged as a space-based infrastructure to offer networking services anywhere on Earth. Satellite IoTs enable novel Direct-to-Satellite (DtS) connectivity, allowing IoT devices in remote areas to connect to the Internet via LEO satellites using existing terrestrial technologies like LoRa. This paper presents the first-of-its-kind measurement study on satellite IoTs, investigating the practical characteristics of DtS communications and their suitability for IoT applications. We deployed 27 low-cost ground stations across eight locations worldwide to passively measure the network availability of multiple constellations. Our findings reveal a significant gap between the effective durations of DtS connectivity and their theoretical durations, leading to intermittent connections for satellite IoTs. Additionally, we examine the performance of the Tianqi constellation in supporting real-world IoT traffic (agriculture application). We observed longer delays and higher power consumption in satellite IoTs compared to terrestrial IoTs. Our study identifies the bottlenecks and sheds light on potential optimizations for satellite IoTs.

LoRaWAN is a prominent technology for Low Power Wide Area Networks (LPWAN). However, the increasing network size has introduced a significant challenge: packet collisions resulting from concurrent transmissions in LoRaWAN. Previous studies either overlooked the issue by examining limited features or tackled it with intricate receivers employing up to eight antennas. To achieve a more favorable balance between implementation cost and system performance, we introduce$\text{Hi}^{2}\text{LoRa}$—a solution utilizing highly dimensional and accurate features for LoRa concurrent decoding, implemented with only two receiving antennas. The feature dimensions are expanded through an exploration of various hardware imperfections and inherent channel state information specific to each transceiver pair. To enhance feature accuracy, low pass filters and BiLSTM networks are applied to capture and learn their temporal patterns. Additionally, an efficient collision suppression strategy is introduced to mitigate feature corruption from concurrently transmitted packets. Extensive real-world testbed evaluations demonstrate that the achievable concurrency in$\text{Hi}^{2}\text{LoRa}$approaches that of state-of-the-art approaches with significantly higher complexity (e.g., utilizing eight antennas) or exceeds prior work by a factor of 2.7 with comparable complexity (e.g., using two antennas).

LoRa has been widely adopted in Internet of Things (IoT) due to its long distance and low cost. With the largescale deployment of LoRa devices, it is not uncommon that multiple nodes transmit concurrently, leading to packet collisions and degraded performance. Previous studies have focused on examining single or multiple features in the received raw signals, named as chirp-level features, to separate collided packets for parallel decoding. However, the accuracy of feature extraction turns out to be vulnerable to interference, which can lead to incorrect packet decoding as network concurrency increases. Our study reveals that, other than the chirp-level features, a standard LoRa packet encoder introduces coding correlations across symbols of the same packet and thus leaves packet-level features to the symbols that can be utilized to disentangle symbols of collided packets in a new dimension. In this work, we introduce DeepDetangle, a Deep-learning-based feature fusion framework that efficiently fuses both packet-level and chirp-level features of symbols to enhance LoRa parallel decoding. DeepDetangle utilizes Complex-CNN and LSTM structures to capture multidimensional chirp-level features, their joint distributions, and temporal patterns. An MLP is employed to aggregate the chirplevel features with the packet-level features, thereby enabling the decoding of symbols on a per-block basis. By integrating features from both levels, erroneous symbols that cannot be recovered with chirp-level features alone can now be effectively corrected with other valid ones in the same block. Therefore, it demonstrates a strong capability to combat interference from other packets. Extensive evaluations have been conducted to assess the performance of DeepDetangle. The results indicate that DeepDetangle achieves$\mathbf{1 8. 1 \%}$to$\mathbf{8 0. 5 \%}$higher network throughput compared to existing works of similar complexity.

LoRa technology has garnered significant attention for its exceptional performance in city-wide applications. LoRa encodes data across multiple samples to enable long-range communication, with the level of redundancy controlled by the Spreading Factor (SF). However, practical limitations restrict how high the SF can be set. To overcome communication challenges at the highest allowable SF settings, we introduce SF-any, a software-based coding paradigm that extends an SFk packet to a quasi-SF(k + m) packet. SF-any encodes a quasi-SF(k + m) symbol with 2mSFk symbols. Hardware imperfections introduce time-varying frequency drifts and phase offsets, resulting in frequency leakage during quasi-SF(k +m) packet decoding. To mitigate this, we strategically insert pilots into the packet for imperfection estimation and compensation. Additionally, to maintain and exploit the coding structure in LoRa PHY, we employ a grouped repetition code at the transmitter and a joint demodulation and decoding scheme at the receiver. Comprehensive evaluations demonstrate that SF-any’s performance seamlessly scales with increasing SF, achieving up to a 14dB improvement over SF12 packets (the highest SF in LoRa PHY), and up to a 12dB improvement compared with state-of-the-art approaches.

Millimeter-wave (mmWave) radar is emerging as a promising sensor for various human sensing tasks. Deep learning is frequently applied in radar-based applications, which typically require extensive data collection and labeling. In this demo, we present a low-cost hardware setup and a cross-platform software pipeline that automatically captures radar data of human activities, labels ground truth, and tests inference models in real time. The effectiveness of the testbed is demonstrated through real-time human pose estimation.

LoRa Wide Area Network (LoRaWAN) is famous for its low power consumption and wide coverage area. A critical issue that constrains the scalability of LoRaWAN is how to support concurrent packet reception efficiently. To this end, our work first carefully investigates all the hardware imperfections between a LoRa transceiver pair and studies their impacts on the received signal's temporal patterns with fine granularity. Such imperfections and the associated temporal patterns are intrinsic and unique for any transceiver pair in a wireless system. Therefore, they provide a brand-new and highly influential perspective for identifying different packets. Motivated by this, we propose Magnifier, which integrates the two factors mentioned above for LoRa concurrent decoding. Though some prior work leverage partial hardware imperfections, they fail to systematically study and exploit the temporal patterns caused by the imperfections for concurrent packet reception. We also evaluate the performance of Magnifier with 40 transmitters (commodity LoRa chips) and one receiver (USRP B210). Both critical complexity analysis and extensive experiments show that with the same hardware settings and the same scale of complexity, Magnifier can support 3× to 8× throughput of other existing schemes.

LoRa Wide Area Network (LoRaWAN) has emerged as a dominant technology for Low Power Wide Area Networks (LPWAN). However, due to the ever-growing network size, packet collisions caused by concurrent transmissions have become a serious challenge in LoRa Wan.Existing studies have either ignored the issue by exploring only a few inaccurate features or addressed it using a complex receiver with up to eight antennas. To strike a better balance between implementation cost and system performance, we propose Hi2LoRa, which leverages highly dimensional and highly accurate features for LoRa concurrent decoding with only two receiving antennas. The feature dimensions are extended by exploring various types of hardware imperfections and channel state information inherent to each transceiver pair. To improve feature accuracy, low pass filters and BiLSTM networks are employed to trace and learn their temporal patterns. Additionally, an effective collision suppression strategy is introduced to combat feature corruption from other concurrent packets. Extensive evaluations on real-world testbeds show that the achievable concurrency in Hi2LoRa is either close to that of state-of-the-art approaches with much higher complexity (e.g., using eight antennas) or 2.7 x of prior work with comparable complexity (e.g., using two antennas).

Time synchronization is a fundamental requirement for wireless communication systems to work properly. Most of the existing studies focus on time synchronization among homogeneous devices. This work investigates time synchronization with heterogeneous technologies (e.g., WiFi, ZigBee, and Bluetooth) which is important for the rising Internet of Thing (IoT) scenarios where heterogeneous devices coexist. Recent advances in cross-technology communication (CTC) break the wall between heterogeneous wireless devices. In this work, we propose a new time synchronization strategy based on the CTC technique and provide a technique called TimeBee, which takes the advantage of coordination from a WiFi device to assist ZigBee devices for time synchronization. An effective method is employed so that ZigBee nodes are coordinated for time synchronization based on the received timestamps from WiFi devices. The experimental results show that TimeBee achieves global time synchronization with low time errors.

As a representative technology of low power wide area network, LoRa has been widely adopted to many applications. A fundamental question in LoRa is how to improve its reception quality in ultra-low SNR scenarios. Different from existing studies that exploit either spatial or temporal correlation for LoRa reception recovery, this paper jointly leverages the fine-grained spatial-temporal correlation among multiple gateways. We exploit the spatial and temporal correlation in LoRa packets to jointly process received signals so that the fine-grained offsets including Central Frequency Offset (CFO), Sampling Time Offset (STO) and Sampling Frequency Offset (SFO) are well compensated, and signals from multiple gateways are combined coherently. Moreover, a deep learning based soft decoding scheme is developed to integrate the energy distribution of each symbol into the decoder to further enhance the coding gain in a LoRa packet. We evaluate our work with commodity LoRa devices (i.e., Semtech SX1278) and gateways (i.e., USRP-B210) in both indoor and outdoor environments. Extensive experiment results show that our work achieves 4.6dB higher signal-to-noise ratio (SNR) and 1.5× lower bit error rate (BER) compared with existing approaches.

Industrial Scientific Medical (ISM) band has become more and more crowded due to the ever-growing size of many mainstream technologies, e.g., Wi-Fi, ZigBee and Bluetooth. Though the coexistence issue has led to severe Cross Technology Interference (CTI), it provides great opportunities to better utilize the scarce bandwidth resources. A fundamental question is how to ensure harmonious and effective operations for different networks To exploit it, a novel global cooperation framework is proposed. In particular, our work enables reliable concurrent Cross Technology Communication (CTC) from Wi-Fi to Wi-Fi, ZigBee and Bluetooth commodity devices. Compared to existing CTC approaches, our scheme improves the communication efficiency significantly, and hence is the foundation for effective global cooperation. Based on the proposed CTC scheme, a unified Media Access Control (MAC) approach is introduced to cooperate CTC message transmission and reception for heterogeneous networks with different MACs. Three proof-of-concepts applications, e.g. global synchronization, global CTI coordination and global Location Based Service (LBS) broadcasting are discussed to fully leverage the benefits of global cooperation. Extensive evaluations show that compared with existing schemes, the proposed framework achieves 8.1 times lower synchronization error, 9 times lower packet delivery delay, and 2.7 times lower energy consumption for acquiring LBS message.

Recent years have witnessed the proliferation of Low-power Wide Area Networks (LPWANs) in the unlicensed band for various Internet-of-Things (IoT) applications. Due to the ultra-low transmission power and long transmission duration, LPWAN devices inevitably suffer from high power Cross Technology Interference (CTI), such as interference from Wi-Fi, coexisting in the same spectrum. To alleviate this issue, this paper introduces the Partial Symbol Recovery (PSR) scheme for improving the CTI resilience of LPWAN. We verify our idea on LoRa, a widely adopted LPWAN technique, as a proof of concept.At the PHY layer, although CTI has much higher power, its duration is relatively shorter compared with LoRa symbols, leaving part of a LoRa symbol uncorrupted. Moreover, due to its high redundancy, LoRa chips within a symbol are highly correlated. This opens the possibility of detecting a LoRa symbol with only part of the chips. By examining the unique frequency patterns in LoRa symbols with time-frequency analysis, our design effectively detects the clean LoRa chips that are free of CTI. This enables PSR to only rely on clean LoRa chips for successfully recovering from communication failures. We evaluate our PSR design with real-world testbeds, including SX1280 LoRa chips and USRP B210, under Wi-Fi interference in various scenarios. Extensive experiments demonstrate that our design offers reliable packet recovery performance, successfully boosting the LoRa packet reception ratio from 45.2% to 82.2% with a performance gain of 1.8×.

Hierarchical Modulation (HM) is quite efficient for multimedia broadcast in wireless networks. It exploits the wireless broadcast advantage and allows a transmission to reach different users with various qualities. However, existing HM related schemes are based on specially designed hardware, leading to extra hardware and deployment cost in practice. As a remedy for this, we design and implement a software-based HM scheme, named as SoftHM. By carefully tuning the Physical Layer input of a packet (a sequence of “0” or “1” bits, known as PHY payload), a transmitter simply broadcasts the packet with a single rate (e.g., 54Mbps, the highest rate in 802.11g WLAN), while diverse receivers can decode and extract different amount of information (with different reception rate, e.g., 12Mbps or 54Mbps) from it based on their link qualities. Reverse engineering techniques are adopted so that SoftHM works in a plug-in mode, offering it high potentials for wireless systems without any changes in existing modulation and coding modules. We implement SoftHM based on Universal Software Radio Peripheral (USRP) B210 and Commercial of the Shelf (COTS) Wi-Fi devices. Both rigorous analysis on its performance and comprehensive implementation-based experiments show that compared with the traditional time-sharing approach, to support low-quality users with the same bandwidth, high-quality users in SoftHM achieve 111.1% to 616.7% higher throughput. Likewise, to serve high-quality users with the same bandwidth, low quality users in softHM achieve 176.9% to 390.9% higher throughput.

Industrial Scientific Medical (ISM) band has become more and more crowded due to the ever-growing size of many mainstream technologies, e.g., Wi-Fi, ZigBee and Bluetooth. Though they compete for limited spectrum resources leading to severe Cross Technology Interference (CTI), it also provides great opportunities to better utilize the scarce bandwidth resources. A fundamental question is how to ensure harmonious and effective operations for these networks? To exploit this issue, a novel global cooperation framework is proposed. In particular, our work enables direct and simultaneous Cross Technology Communication (CTC) from a single Wi-Fi to ZigBee, Bluetooth and Wi-Fi commodity devices sharing the same band. Compared to existing CTC approaches, our scheme improves the communication efficiency significantly, and hence is the foundation for effective global cooperation. Based on the proposed CTC scheme, a unified Media Access Control (MAC) approach is introduced to cooperate CTC message transmission and reception for heterogeneous devices with different MACs. Two proof-of-concepts applications, e.g. global synchronization and global CTI coordination are discussed to fully leverage the benefits of global cooperation. Extensive evaluations show that compared with existing schemes, the proposed framework achieves 13 times lower synchronization error and 9 times lower average packet delay in CTI intensive environments.

Accurate link latency information is essential for various traffic engineering problems, such as routing design and network diagnosis. However, due to the routing constraints, link latency measurement is still a challenging problem in hybrid software-defined networks (SDNs) and little literature has been found in this field. Due to cost, a hybrid SDN composed of conventional routers and SDN switches will exist for some time to come, which shows the necessity and the urgency to address this problem. In this paper, we investigate this link latency measurement problem in two different scenarios: 1) the conventional routers can only support the shortest path routing protocol, and 2) the conventional routers can support source routing protocol. For both of these scenarios, we show how to deploy a minimum number of monitors and how to construct measurement paths between monitors to measure all the link latencies. Several algorithms are presented to solve these problems and the evaluations on different topologies prove the superiority of the proposed methods.

Accurate link latency information is required for solving many traffic engineering problems, such as congestion avoidance routing and load balancing. Many link latency measurement schemes have been proposed for Software-Defined Networks (SDNs), and many of these require the SDN controllers to inject and remove entries into and from flow tables dynamically. If done frequently, this process can generate a large amount of traffic overhead and consume a lot of processing and computation resources. In this paper, we present a link latency measurement scheme, where entries for latency measurement are already embedded inside flow tables. The main challenge in designing such a scheme lies in how to select measurement paths that can minimize the number of entries added to the expensive TCAM-based flow tables and the amount of traffic generated for latency measurement. We present solutions that can minimize both in this paper.

Short-term traffic flow prediction plays a crucial component in transportation management and deployment. In this paper, a novel regression framework for short-term traffic flow prediction with automatic parameter tuning is proposed, with the SVR being the primary regression model for traffic flow prediction and the Bayesian Optimization being the major method for parameters selection. First, the preprocessing of raw traffic flow is carried out by seasonal difference to eliminate the non-stationary of the data. Then, Support Vector Regression model is trained by the pre-processed data. In order to optimize the model parameters, the generalization performance of SVR is modeled as a sample from a Gaussian process (GP). Bayesian optimization determines the parameters configuration of the regression model by optimizing the acquisition function over the GP. Finally, the optimal short-term traffic flow regression model is constructed through repeated GP update and iteratively multiple training of the model. Experiment results show that the accuracy of proposed method is superior to methods of classical SARIMA, MLP-NN, ERT and Adaboost.

Mobile Edge Cloud Computing (MECC) has becoming an attractive solution for augmenting the computing and storage capacity of Mobile Devices (MDs) by exploiting the available resources at the network edge. In this work, we consider computation offloading at the mobile edge cloud that is composed of a set of Wireless Devices (WDs), and each WD has an energy harvesting equipment to collect renewable energy from the environment. Moreover, multiple MDs intend to offload their tasks to the mobile edge cloud simultaneously. We first formulate the multi-user multi-task computation offloading problem for green MECC, and use Lyaponuv Optimization Approach to determine the energy harvesting policy: how much energy to be harvested at each WD; and the task offloading schedule: the set of computation offloading requests to be admitted into the mobile edge cloud, the set of WDs assigned to each admitted offloading request, and how much workload to be processed at the assigned WDs. We then prove that the task offloading scheduling problem is NP-hard, and introduce centralized and distributed Greedy Maximal Scheduling algorithms to resolve the problem efficiently. Performance bounds of the proposed schemes are also discussed. Extensive evaluations are conducted to test the performance of the proposed algorithms.

Accurate traffic matrix estimation is critical to solving many networking problems, such as routing and network provisioning, etc. This task in a traditional IP network is tackled with link load measurements. But the accuracy of such an approach is low because the underlying system of linear equations governing the traffic estimation problem is highly under-determined in this approach. A software-defined network (SDN) provides measurements of more types of flows, and thus opens up new opportunities for tackling the traffic matrix estimation problem. Furthermore, the controller of an SDN can dynamically install flows in the flow table of an SDN router for traffic measurement. But there is one constraint: the number of entries in a flow table for traffic measurement is limited because the flow table in an SDN node is usually constructed with expensive ternary content addressable memory (TCAM) devices. How to use the limited number of entries in a flow table for traffic measurement is a challenging task in SDNs. In this paper, we present a new framework for solving the traffic matrix estimation problem in an SDN-based IP network. An important feature of the proposed framework is that any flow added to the flow table of an SDN router can increase the rank of the underlying equations governing the traffic matrix estimation problem. This greatly improves the utilization efficiency of TCAM entries for traffic measurement. Detailed performance evaluation given in this paper demonstrates that the proposed approach can achieve significant performance gains over other approaches.

Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud/cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.

Recently, the modeling and design of distributed controllers in cyber-physical systems (CPSs), which suffer from messages lost, delay variation, and jitter, has gained lots of research attentions. A special CPS, arbitrated networked control system (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, we propose a novel ANCS with dual communication channels. The proposed ANCS uses a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy that is based on the event trigger protocol. A flat semi-dormant multicontrollers (FSDMC) model is developed for the proposed ANCS. We then model the FSDMC as an N/(d,c)-M/M/c/K/SMWV queue, and obtain various performance indices. Based on the model, a multiobjective optimization problem is then formulated to minimize the nonlinear energy consumption function and the nominal delay function presented in this study. To resolve the multiobjective optimization problem, a scheduling algorithm based on the multiobjective particle swarm optimization algorithm is proposed to generate the Pareto front and the corresponding nondominated vector sets. An optimal stopping algorithm is also designed to obtain the optimal value of the number of semi-dormant controllers. The optimal values of various parameters of the control system are obtained by using the above nondominated vector sets, and are applied to the proposed ANCS. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and the effects of the control system parameters on the optimal policy.

In spatial time division multiple access wireless mesh networks, not all links can be activated simultaneously, as links scheduled for transmission must satisfy the specified SINR requirements. Previously, slot assignment has been done on a link basis, where a set of links is selected for transmission in a given slot. However, if selected links are in deep fade or have no traffic to transmit, the slot is wasted. Thus, a node-based scheme was proposed, where a set of nodes is selected for transmission. Which link to be used by a node depends on the links' instantaneous traffic load. Although this allows us to exploit multi-user diversity, it creates a planning discrepancy: slot assignment is designed based on long-term channel statistics, but scheduling on short-term channel fading conditions. Consequently, the performance gain of the node-based scheme is not consistent: it is marginal under certain scenarios. To avoid the design discrepancy, we develop a new slot-assignment and routing framework in this paper. The new approach incorporates short-term channel fading statistics to optimize the long term slot assignment, routing and scheduling simultaneously. Hence, multi-user diversity can be exploited more efficiently. Not only is the performance gain of the resulting system significant (can be as much as 64% higher throughput than the scheme introduced by Chen and Lea), it is also less topology dependent compared with the one by Chen and Lea.

Interference management is a fundamental issue of a wireless network. In this paper, an interference management scheme based on partial loading is presented. The advantage of partial loading is that it does not require any protocol changes and renders an easy implementation. The partial loading scheme can be combined with other interference management techniques. In this paper, we present various partial loading schemes and evaluated their performances. These schemes divide the total channels into subgroups. By intelligently select the loading probability for each subgroup, we show that the performance, in terms of coverage and spectrum efficiency, of the network can be greatly improved. An analytical model is developed in the paper to verify the performance gains of these proposed partial loading schemes.