Comparative study for broadband direction of arrival estimation techniques

This paper reviews and compares three different linear algebraic signal subspace techniques for broadband direction of arrival estimation --- (i) the coherent signal subspace approach, (ii) eigenanalysis of the parameterised spatial correlation matrix, and (iii) a polynomial version of the multiple signal classification algorithm. Simulation results comparing the accuracy of these methods are presented

Broadband angle of arrival estimation methods in a polynomial matrix decomposition framework

A large family of broadband angle of arrival estimation algorithms are based on the coherent signal subspace (CSS) method, whereby focussing matrices appropriately align covariance matrices across narrowband frequency bins. In this paper, we analyse an auto-focussing approach in the framework of polynomial covariance matrix decompositions, leading to comparisons to two recently proposed polynomial multiple signal classification (MUSIC) algorithms. The analysis is complemented with numerical simulations

Edge Caching in Dense Heterogeneous Cellular Networks with Massive MIMO Aided Self-backhaul

This paper focuses on edge caching in dense heterogeneous cellular networks (HetNets), in which small base stations (SBSs) with limited cache size store the popular contents, and massive multiple-input multiple-output (MIMO) aided macro base stations provide wireless self-backhaul when SBSs require the non-cached contents. Our aim is to address the effects of cell load and hit probability on the successful content delivery (SCD), and present the minimum required base station density for avoiding the access overload in an arbitrary small cell and backhaul overload in an arbitrary macrocell. The massive MIMO backhaul achievable rate without downlink channel estimation is derived to calculate the backhaul time, and the latency is also evaluated in such networks. The analytical results confirm that hit probability needs to be appropriately selected, in order to achieve SCD. The interplay between cache size and SCD is explicitly quantified. It is theoretically demonstrated that when non-cached contents are requested, the average delay of the non-cached content delivery could be comparable to the cached content delivery with the help of massive MIMO aided self-backhaul, if the average access rate of cached content delivery is lower than that of self-backhauled content delivery. Simulation results are presented to validate our analysis.Comment: Accepted to appear in IEEE Transactions on Wireless Communication

Completion-Time-Driven Scheduling for Uplink NOMA-Enabled Wireless Networks

Efficient scheduling policy is crucial in wireless networks due to delay-sensitivity of many emerging applications. In this work, we consider a joint user pairing and scheduling (UPaS) scheme for multi-carrier non-orthogonal multiple access (MC-NOMA)-enabled wireless networks to reduce the maximum completion time of serving uplink users. The NOMA scheduling problem is shown to be NP-hard and a shortest processing time (SPT)-based strategy to solve the same problem within affordable time and complexity is introduced. The simulation results confirm the efficacy of the proposed scheduling scheme in terms of the maximum completion time in comparison with orthogonal multiple access (OMA) and random NOMA pairing

Safeguarding Massive MIMO Aided HetNets Using Physical Layer Security

This paper exploits the potential of physical layer security in massive multiple-input multiple-output (MIMO) aided two-tier heterogeneous networks (HetNets). We focus on the downlink secure transmission in the presence of multiple eavesdroppers. We first address the impact of massive MIMO on the maximum receive power based user association. We then derive the tractable upper bound expressions for the secrecy outage probability of a HetNets user.We show that the implementation of massive MIMO significantly improves the secrecy performance, which indicates that physical layer security could be a promising solution for safeguarding massive MIMO HetNets. Furthermore, we show that the secrecy outage probability of HetNets user first degrades and then improves with increasing the density of PBSs

Auction based competition of hybrid small cells for dropped macrocell users

We propose an auction based beamforming and user association algorithm for a wireless network consisting of a macrocell and multiple small cell access points (SCAs). The SCAs compete for serving the macrocell base station (MBS) users (MUs). The corresponding user association problem is solved by the proposed bid-wait auction (BWA) method. We considered two scenarios. In the first scenario, the MBS initially admits the largest possible set of MUs that it can serve simultaneously and then auctions off the remaining MUs to the SCAs, who are willing to admit guest users (GUs) in addition to their commitments to serve their own host users (HUs). This problem is solved by the proposed forward bid-wait auction (FBWA). In the second scenario, the MBS aims to offload as many MUs as possible to the SCAs and then admits the largest possible set of remaining MUs. This is solved by the proposed backward bid-wait auction (BBWA). The proposed algorithms provide close to optimum solution as if obtained using a centralised global optimization

Support Vector Machine for Network Intrusion and Cyber-Attack Detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Cyber-security threats are a growing concern in networked environments. The development of Intrusion Detection Systems (IDSs) is fundamental in order to provide extra level of security. We have developed an unsupervised anomaly-based IDS that uses statistical techniques to conduct the detection process. Despite providing many advantages, anomaly-based IDSs tend to generate a high number of false alarms. Machine Learning (ML) techniques have gained wide interest in tasks of intrusion detection. In this work, Support Vector Machine (SVM) is deemed as an ML technique that could complement the performance of our IDS, providing a second line of detection to reduce the number of false alarms, or as an alternative detection technique. We assess the performance of our IDS against one-class and two-class SVMs, using linear and non-linear forms. The results that we present show that linear two-class SVM generates highly accurate results, and the accuracy of the linear one-class SVM is very comparable, and it does not need training datasets associated with malicious data. Similarly, the results evidence that our IDS could benefit from the use of ML techniques to increase its accuracy when analysing datasets comprising of non-homogeneous features

Robust waveform design for multistatic cognitive radars

In this paper we propose robust waveform techniques for multistatic cognitive radars in a signal-dependent clutter environment. In cognitive radar design, certain second order statistics such as the covariance matrix of the clutter, are assumed to be known. However, exact knowledge of the clutter parameters is difficult to obtain in practical scenarios. Hence we consider the case of waveform design in the presence of uncertainty on the knowledge of the clutter environment and propose both worst-case and probabilistic robust waveform design techniques. Initially, we tested our multistatic, signaldependent model against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered scenario. We therefore derived a new approach where we assume uncertainty directly on the radar cross-section and Doppler parameters of the clutters. Accordingly, we propose a clutter-specific stochastic optimization that, by using Taylor series approximations, is able to determine robust waveforms with specific Signal to Interference and Noise Ratio (SINR) outage constraints

A novel blind equalization structure for deep null communication channels

A new blind equalization structure that is well suited for communication channels whose zeros are close to the unit circle is proposed. Most blind equalizers which operate at the baud rate perform poorly for channels whose maximum phase zeros are close to the unit circle. This limitation is mainly due to the inability to model the inverse of such maximum phase zeros with a finite length filter. Our proposed structure adaptively models the inverse channel, completely, without the need to transmit a training sequence. Therefore Inter Symbol Interference (ISI) is removed even if the channel has deep spectral nulls. Another attractive feature of this structure is that it estimates the channel parameters directly, and as such may be used with “indirect” equalization techniques. Simulation studies are included to demonstrate the performance of the schem

Space-time block coding for four transmit antennas with closed loop feedback over frequency selective fading channels

Orthogonal space-time block coding is a transmit diversity method that has the potential to enhance forward capacity. For a communication system with a complex alphabet, full diversity and full code rate space-time codes are available only for two antennas, and for more than two antennas full diversity is achieved only when the code rate is lower than one. A quasi-orthogonal code could provide full code rate, but at the expense of loss in diversity, which results in degradation of performance. We propose a closed loop feedback scheme for quasi-orthogonal codes which provides full diversity while achieving the full code rate. We investigate, in particular, the performance of this scheme, when the feedback information is quantised and when the fading of the channel is frequency-selective