In this paper, we study the problem of mobile user pairing in non-orthogonal multi-access (NOMA) backscatter communication networks, in which re-pairing users after a change in the communication link can bring significant computational overhead. Firstly, We propose a user pairing scheme that employs the Kuhn-Munkres (KM) algorithm for the uplink of the monostatic backscatter communication network, which can effectively avoid the problem of frequent sorting mobile users. Secondly, to address the NOMA principle violations problem (NPVP) caused by mobile users, we propose a modified role switching (RS) technique based on the difference of user channel gain in which paired users switch their roles based on the magnitude of their channel gain after moving. Finally, we propose a dynamic power allocation scheme for mobile users to maximize the system throughput after pairing. In backscatter communication, the difference in power levels between paired users is achieved by setting different reflection coefficients. At the same time, it is necessary to consider the balance between data transmission and energy harvesting, and the reflection coefficient must not exceed the predetermined upper limit. Simulation results indicate that our proposed user pairing scheme outperforms conventional schemes in terms of the number of decoded users and system throughput.

Millimeter wave radar-based human activity recognition (HAR) technology has received much attention as a research hot-spot in recent years. Previous researches have demonstrated the feasibility of using millimeter wave radar for HAR. While existing work has achieved excellent performance in ideal environments, its application in life is still limited due to the intensive data collection required, the additional training needed to adapt to new domains (i.e., environments, people, and locations), and the high computational complexity associated with voxelization. To solve the above problems, we propose the radar point cloud recognition system RPCRS, which is capable of accurately recognizing human activities from noisy environments, has promising recognition performance for new users, environments and locations, and significantly reduces the computational overhead during system training. Firstly, RPCRS use the velocity information of the clustered point cloud data to extract the human activity subjects from the noisy background. Then, the size of the extracted non-uniform point cloud data is unified by removing or adding the number of point clouds. Secondly, in order to enhance the robustness of the system and reduce the data collection effort, we designed a data enhancement framework based on correlation between point cloud data and human activity changes. Finally, a lightweight neural network based on a multilayer perceptron (MLP) is used to classify the raw point cloud data of human activities, which reduces the computational complexity and memory requirements associated with voxelization. We evaluate our system with 5 different activities, which attains average accuracy of 95.40%. In addition, we evaluate the performance of the system in a new environment and with new users, which obtains an average accuracy of 94.53% and 95.08%, respectively.

In this paper, we investigate the user pairing schemes for NOMA-enabled backscatter communication network. Since the successful decoding of high channel gain users can significantly improve the system performance, to make more high channel gain users be decoded successfully, we propose a pairing scheme, M-UPS, which is based on the user channel gain difference. Firstly, in the case of two-user pairing, we divide users into the high channel gain group and the low channel gain group based on channel gain, then we provide a formula of the pairing distance threshold for the high channel gain user to select low channel gain users for pairing. Secondly, we extend the proposed paring scheme to the multi-user case, a dynamic user pairing scheme, which can adaptively form the different pairs with different numbers of users based on the channel gain differences. Simulation results show that M-UPS can outperform the Conventional-NOMA (C-NOMA), Uniform Channel Gain Difference-NOMA (UCGD-NOMA), and OMA in terms of the number of paired users and total throughput.

Interference alignment (IA) is a promising technique to effectively manage the interference. The realization of IA requires a proliferation of feedback bits. In a general centralized feedback model, the feedback rate of the precoder and the decoder affects the performance of the IA directly. In this paper, to improve the feedback efficiency of the precoder and the decoder, a strategy that can adaptively allocate the feedback bits of the precoder and the decoder is proposed. We consider the effect of link loss on the throughput loss caused by the quantization error of the precoder and decoder. An upper bound of leaked interference as a function of the link loss and the feedback bits of the precoders and the decoders is derived. The feasibility conditions for dynamically allocating the feedback bits of the precoder and the decoder are analyzed. It is proven that the properties of general asymmetric interference network can satisfy the feasibility conditions for dynamic feedback scheme. According to the simulation results, our proposed scheme achieves higher throughput compared with the conventional schemes in the asymmetric interference network.

Rate adaptation, which dynamically chooses transmission rate provided at the physical layer according to the current channel conditions, is a fundamental resource management issue in IEEE 802.11 networks with the goal of maximizing the network throughput. Traditional rate adaptation algorithms for IEEE 802.11n networks do not consider the interference problem, which becomes much more serious due to the rapid deployment of IEEE 802.11n devices and large number of mobile terminals. In this paper, an interference-aware rate and channel adaptation scheme RaCA for intensive IEEE 802.11n networks was proposed. Firstly, RaCA leverages RSSI and CSI information together to measure the current channel conditions at the receiver side. RSSI is a coarse-grained indicator and CSI is a fine-grained indicator. Secondly, a two-stage rate adaptation scheme TSRA was designed, which can quickly adapt to optimal bit rate based on RSSI and CSI information. Finally, a quorum-based channel adaptation algorithm QCA was proposed, which does not need control channel. If channel suffers severe interferences, RaCA calls QCA to choose another channel to work on. Simulation and testbed implementation results demonstrate that RaCA achieves significant throughput gain over SampleLite and Minstrel-HT.

It is well known that there are two kinds of causes, namely channel-errors and collisions, which lead to high probability of packet losses and errors in wireless networks. The ability of discriminating the above two causes provides many opportunities for implementing high efficient networking protocols in wireless sensor networks (WSNs). This paper presents EasiPLED, a discriminator that can accurately and timely predict these two causes. EasiPLED has three salient features. First, it investigates F-BER patterns and statistic characteristics of RSSI in different indoor environments through extensive experimental studies. F-BER is the Frame-level Bit Error Rate measured at the receiver side by a coarse-grained method without incurring any overhead. An adaptive RSSI estimator based on error-based filter is proposed to mitigate effects of noise on RSSI readings for successfully received packets. Second, EasiPLED designs an off-line dominant-factor classifier using machine learning method. The classifier takes a combination of F-BER and RSSI features as input and outputs the probability of dominant causes of failed transmissions. Finally, it presents a lightweight on-line discriminator which diagnoses the root cause of a packet loss or error when it occurs at the receiver side. Experimental results show that EasiPLED achieves an accuracy by up to 95.4%. We evaluate the effectiveness of EasiPLED by applying it to link-layer retransmission scheme, which yields a reduction of single-hop transmission delay by up to 47%, and provides high packet delivery ratios as compared to the existing retransmission methods.

This paper focuses on the design of high-throughput MAC for data-intensive sensor networks with unpredictable traffic. We propose Log A, a reactive concurrent MAC protocol that increases transmission concurrency based on time log analysis. Each node reactively and passively learns the interference relationship by analyzing the start transmission time and the end transmission time of packet blocks. Then, the learned interference relationship is exploited to improve the probability of beneficial concurrent transmissions of nodes that are within the interference range of each other. Log A has two salient features. First, it is passive and does not need network downtime to build interference relationship. Second, it is reactive and works only when traffic are generated. Log A has been implemented in Tinyos-2.1 and extensively evaluated in TOSSIM, the simulator of sensor networks. Experimental result shows that Log A outperforms the traditional CSMA protocol and an existing reactive concurrent MAC in terms of throughput, delivery latency, and energy consumption.

Rate adaptation, which adapts transmission bit rate according to current wireless link conditions, is a fundamental mechanism used by link-layer protocols to improve the performance of 802.11 wireless access networks in terms of throughput. However, rate adaptation faces to severe challenges due to more and more congested and dynamic wireless links. In this paper, we design a hybrid rate adaptation scheme, called EasiRA, for 802.11 mobile wireless access networks. It has following three features. First, it combines the sensor-hints and protocol-hints information together to estimate current link status. Second, EasiRA exploits environmental signal strength information obtained by a 802.15.4-based radio to help distinguish the causes of packet losses and adjust the thresholds of the protocol-hints. Finally, EasiRA uses both random and deterministic rate increase or decrease schemes to combat the dynamic and unpredictable characteristics of wireless links. Simulation results show that EasiRA consistently outperforms the existing rate adaptation schemes, namely CARA, Minstrel and RapidSample, particularly in relatively high dynamic scenario.