Multicarrier code-division multiple access (MC-CDMA) is a promising candidate for the air interface of future wireless communication systems. After the development of a unified signal model that applies to the different multirate MC-CDMA schemes studied in this paper, a novel space-frequency multiuser detection scheme for multirate uplink MC-CDMA systems with multiple receive antennas is proposed. In particular, a space-frequency minimum mean-squared error (MMSE)-based hybrid serial/parallel interference cancellation receiver (SF-MMSE/HIC) is developed which produces the SF-MMSE combiner as a byproduct. The signal processing of this new detector is jointly carried out in space and frequency domains which leads to the advantage of combating the interference resulting from different sources simultaneously. Based on subspace estimation, a semiblind implementation of the receiver is introduced for slow fading channels. Furthermore, the impact of subcarrier interleaving is investigated for variable spreading factor (VSF) MC-CDMA systems. Based on the characteristic function (CF) of a complex Gaussian random vector (CGRV), a method for analyzing the bit error probability (BEP) performance is presented. Several representative simulation examples of the detector are provided to demonstrate the performance of the proposed receiver. In a fully loaded system, the proposed receiver can offer performance close to the single-user bound. Moreover, the results indicate that multicode and VSF MC-CDMA schemes give very similar performance
In this paper, we propose a novel index modulation mechanism called permutation channel modulation (PCM) by exploiting spatial resource of multiple input multiple output (MIMO). A set of permutation matrices is treated as indices to convey information bits by modulating a block of bits to a permutation matrix. Assuming that channel state information at transmitter (CSIT) is known, modulated permutation matrix is multiplied to the singular values of MIMO channel matrix obtained from decomposing the channel matrix using singular values decomposition (SVD). Besides to a permutation matrix, information bits are also modulated to constellation symbols. Therefore, the transmitted signals contain two sources of information: a permutation matrix and constellation symbols. At the receiver, transmitted symbols and a permutation matrix are detected using our proposed detection scheme. We derive the capacity expression and define the optimal capacity by finding the optimal power allocation at each transmission. As performance measure, capacity of PCM is compared with existing techniques. Using 4 × 4, we find that the capacity of PCM is doubled and remains superior with higher antenna settings. Our work creates a new paradigm of index modulation for multiple antenna transmissions by exploiting its spatial resource and significantly improves the capacity and outage probability performances.
The IEEE 802.11 has been shown to be quite inefficient in multihop networks. Besides the problems of hidden terminal and exposed terminal, there is also unreachability problem, which may result in link/routing failures and unfairness among multiple traffic flows. In this paper, a novel medium access control (MAC) protocol, called eMAC, is proposed. Under the proposed scheme, stations maintain double hop neighborhood (DHN) graphs while exchanging designated eMAC tables to share their knowledge about their neighbourhood topology. Using a DHN graph and an adaptive unreachability reporting mechanism, stations are reliably informed about their neighbours' unreachability status; therefore, they avoid establishing link layer connections with their unreachable neighbours and consequently, network resources are not consumed for unsuccessful connection establishment efforts. Furthermore, we propose an adaptive table broadcasting technique to facilitate topology information dissemination in mobile ad hoc networks. Performance of the proposed schemes is evaluated and compared with earlier schemes through simulations.
In this paper we investigate the applicability of inband full-duplex (FD) radios in device-to-device communication (D2D) in cellular systems. The key challenge in FD radio design is the large self-interference on receiver. It is not possible to completely cancel the self-interference in FD radios. Considering the residual of self-interference, we study the performance of system while D2D link is operating in half-duplex and full-duplex mode. A cellular system is considered while D2D users are exploiting the same resources with cellular users in uplink. We apply a power control method for D2D pair to limit the interference from them on base station. For interference management from cellular transmissions on D2D receivers, interference limited area method is used. Simulation results show that using the most recent FD radios in D2D systems can almost double the throughput of the D2D link.
In this paper, we develop a collaborative positioning mechanism which uses Bayesian probabilistic models to combine multidimensional sensory data and localize target nodes over the network deployment area. Herein, heterogeneous anchor nodes with distinct radio access technologies and experiencing various radio channel features implement a joint sensor fusion and positioning system for industry verticals. The proposed mechanism also relies on a modern network architecture whereby devices offload high-demand computation to more capable edge servers which then estimate the target node position after gathering anchors measurements and prior history. Kernel density estimation results are used to show that edge servers implementing Bayesian-based sensor fusion and positioning system effectively estimate the target node location when using hybrid metrics and combining past and current sensory inputs.
In this paper, we present a novel simple genetic algorithm (GA) based multiuser detector for multicarrier code-division multiple-access (MC-CDMA) systems. More specifically, after getting the initial population from the front-end frequency domain linear minimum mean-squared error (MMSE) equalizer, simple GA operators are applied to expand the candidate list and the maximum likelihood (ML) search is implemented in the list. Compared to the MMSE receiver, the proposed receiver can provide superior performance with very little increase in complexity. Moreover, in order to test the robustness of the receiver, the impacts of channel estimation errors are studied by incorporating a semi-blind subspace channel estimator in the receiver. Simulation results also demonstrate its robustness to channel estimation errors.
We introduce a model for capturing the effects of beam misdirection on coverage and throughput in a directional wireless network using stochastic geometry. In networks employing ideal sector antennas without sidelobes, we find that concavity of the orientation error distribution is sufficient to prove monotonicity and quasi-concavity (both with respect to antenna beamwidth) of spatial throughput and transmission capacity, respectively. Additionally, we identify network conditions that produce opposite extremal choices in beamwidth (absolutely directed versus omni-directional) that maximize the two related throughput metrics. We conclude our paper with a numerical exploration of the relationship between mean orientation error, throughput-maximizing beamwidths, and maximum throughput, across radiation patterns of varied complexity.
The linear minimum mean-squared-error (LMMSE) criterion can be used to obtain near-far resistant receivers in direct-sequence code-division multiple-access systems. The standard version of the LMMSE receiver (postcombining LMMSE) minimizes the mean-squared error between the filter output and the true transmitted data sequence. Since the detector depends on the channel coefficients of all users, it cannot be implemented adaptively in fading channels due to severe tracking problems. A modified criterion for deriving LMMSE receivers (precombining LMMSE) in fading channels is presented. The precombining LMMSE receiver is independent of the users' complex channel coefficients, and it effectively converts the time-varying Rayleigh fading channel to an equivalent fixed additive white Gaussian noise channel from the point of view of updating the detector. The performance of the LMMSE receivers in fading channels is studied via computer simulations and numerical analysis. The results show that the postcombining LMMSE receiver has potentially larger capacity, but it cannot be used in fast fading channels. The precombining LMMSE receiver has slightly worse capacity than the postcombining LMMSE receiver, but remarkably larger capacity than the conventional RAKE receiver at the signal-to-noise ratios of practical interest.
No abstract is provided for this article.
The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.
We propose a robust beamforming design for underlay cognitive radio networks where multiple secondary transmitters communicate with corresponding secondary receivers and coexist with a primary network. We consider a scenario where all transmitters have multiple antennas and all primary and secondary receivers are equipped with a single antenna. The main focus is to design the optimal transmit beamforming vectors for secondary transmitters that maximize the minimum of the received signal-to-interference-plus-noise ratios of the cognitive users. The interference powers to the primary receivers are kept below a threshold to guarantee that the performance of the primary network does not degrade due to the secondary network. Imperfect channel state information (CSI) in all relevant channels are considered, and a bounded ellipsoidal uncertainty model is used to model the CSI errors. We recast the problem in the form of semidefinite program and an iterative algorithm based on the bisection method is proposed to achieve the optimal solution. Further, we propose upper and lower bounds for the optimal value of the considered problem, which provides better initialization for the algorithm. Numerical simulations are conducted to show the effectiveness of the proposed method against the non-robust design.
