Antijamming Schemes for the Generalized MIMO Y Channel.

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input-multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel.

A QoS-Adaptive Interference Alignment Technique for In-Band Full-Duplex Multi-Antenna Cellular Networks.

In this paper, we propose a novel interference alignment (IA) technique for an in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) cellular network where a base station (BS) and user equipment (UE) are equipped with multiple antennas, and the local channel state information (CSI) is available at all nodes. Considering a practical IBFD MIMO cellular network, it is assumed that only the BS operates with full-duplex (FD) communication while UE operate in half-duplex (HD) mode. These IBFD networks introduce a new type of interference called cross-link interference (CLI), in which uplink UE affects downlink UE. The proposed IA technique consists of two symmetric IA schemes according to the number of antennas in the uplink and downlink UE, and both schemes effectively mitigate CLI in the IBFD MIMO network. It is worth noting that both IA schemes are adaptively applicable according to the network's quality-of-service (QoS) requirements, such as uplink and downlink traffic demands. Furthermore, we theoretically characterize and prove the achievable sum-degrees-of-freedom (DoF) of the proposed IA technique. Simulation results show that the proposed IA technique significantly improves the sum rate performance compared to conventional HD communications (multi-user MIMO) while achieving the same achievable DoF as the interference-free IBFD MIMO network.

On the Application of K-User MIMO for 6G Enhanced Mobile Broadband.

This paper presents a high-throughput wireless access framework for future 6G networks. This framework, known as K-User MIMO, facilitates all-to-all communication between K access points and K mobile devices. For such a network, we illustrate the demodulation of K2 independent data streams through a new interference cancellation beamforming algorithm that improves spectral efficiency compared to massive MIMO. The paper derives a multi-user Shannon Capacity formula for K-User MIMO when K is greater than or equal to 3. We define an Orthogonal Frequency Division Multiplexing (OFDM) frame structure that demonstrates the allocation of time-frequency resources to pilot signals for channel estimation. The capacity formula is then refined to include realistic pilot overheads. We determine a practical upper-bound for MIMO array sizes that balances estimation overhead and throughput. Lastly, simulation results show the practical capacity in small cell geometries under Rayleigh Fading conditions, with both perfect and realistic channel estimation.

Opportunistic Interference Alignment for Spectrum Sharing between Radar and Communication Systems.

In this paper, we propose a novel opportunistic interference alignment technique for spectrum-shared radar and uplink cellular communication systems where both systems are equipped with multiple antennas. In the proposed OIA technique, the radar system sends its signal so that the radar signal is received into interference space at base stations (BSs) of the cellular system, while each uplink user (UE) generates its transmit beamforming vector so that communication signals are received within interference space at the radar receiver. Moreover, to achieve better sum-rate performance of the cellular communication system, the BS selects the UEs which results in sufficiently small interference to other cells for the uplink communication. With the proposed OIA technique, detection performance of the radar system is protected, while the communication system achieves satisfactory sum-rate performance. Through extensive computer simulations, we show that the performances of both radar and communication systems with the proposed technique significantly outperform a conventional null-space projection based spectrum sharing scheme.

An Analytical Model for Interference Alignment in Broadcast Assisted VANETs.

Application of safety-related information interaction among vehicles has always been a research frontier in Vehicular Ad-hoc NETworks (VANETs). These messages require high real-time performance. There is a lot of research dependant on creating optimization model for communication task scheduling or routing protocols to reduce communication delay. In this paper, we analyze characteristics of safety-related information and introduce Interference Alignment (IA) technology in VANETs. To further improve routing efficiency, a data-driven assisted transmission routing and broadcast model framework for Vehicle to Vehicle(V2V) and Vehicle to Infrastructure (V2I) communication are constructed which are the basis for IA. Depending on the proposed model, we propose an optimization problem of minimizing total number of time slots required for safety information sharing in VANETs. Then a clustering algorithm is designed to narrow feasible solution space. Simulation results show that the approach can effectively reduce the number of time slots required and improve link use by 20% percent compared with no IA applied.

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks.

This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.

Joint Interference Alignment and Power Allocation for K-User Multicell MIMO Channel through Staggered Antenna Switching.

In this paper, we characterise the joint interference alignment (IA) and power allocation strategies for a K-user multicell multiple-input multiple-output (MIMO) Gaussian interference channel. We consider a MIMO interference channel with blind-IA through staggered antenna switching on the receiver. We explore the power allocation and feasibility condition for cooperative cell-edge (CE) mobile users (MUs) by assuming that the channel state information is unknown. The new insight behind the transmission strategy of the proposed scheme is premeditated (randomly generated transmission strategy) and partial cooperative CE MUs, where the transmitter is equipped with a conventional antenna, the receiver is equipped with a reconfigurable multimode antenna (staggered antenna switching pattern), and the receiver switches between preset T modes. Our proposed scheme assists and aligns the desired signals and interference signals to cancel the common interference signals because the received signal must have a corresponding independent signal subspace. The capacity for a K-user multicell MIMO Gaussian interference channel with reconfigurable multimode antennas is completely characterised. Furthermore, we show that the proposed K-user multicell MIMO scheduling and K-user L-cell CEUs partial cooperation algorithms elaborate the generalisation of K-user IA and power allocation strategies. The numerical results demonstrate that the proposed intercell interference scheme with partial-cooperative CE MUs achieves better capacity and signal-to-interference plus noise ratio (SINR) performance compared to noncooperative CE MUs and without intercell interference schemes.

A Novel Codeword Selection Scheme for MIMO-MAC Lower-Bound Maximization.

Aiming at the limitations of the existing Limited Feedback Interference Alignment algorithms, this paper proposes a direct codeword selection scheme that maximizes the lower-bound of the user rate and reduces the sum rate loss by integrating the Bit Allocation algorithm. The target signal is decoded using the maximum signal to interference plus noise ratio (MAX-SINR) algorithm. Moreover, low complexity and global searching mechanisms are deployed to select the optimized codewords from the generated sets of codewords that approach the ideal precoder. Simulation results show that the proposed algorithm effectively improves the rate lower-bound of the system user as compared with the existing state-of-the-art algorithms.

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks.

It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput.

Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel.

In this paper, we study the emergence of topological interference alignment and the characterizing features of a multi-user broadcast interference relay channel. We propose an alternative transmission strategy named the relay space-time interference alignment (R-STIA) technique, in which a K -user multiple-input-multiple-output (MIMO) interference channel has massive antennas at the transmitter and relay. Severe interference from unknown transmitters affects the downlink relay network channel and degrades the system performance. An additional (unintended) receiver is introduced in the proposed R-STIA technique to overcome the above problem, since it has the ability to decode the desired signals for the intended receiver by considering cooperation between the receivers. The additional receiver also helps in recovering and reconstructing the interference signals with limited channel state information at the relay (CSIR). The Alamouti space-time transmission technique and minimum mean square error (MMSE) linear precoder are also used in the proposed scheme to detect the presence of interference signals. Numerical results show that the proposed R-STIA technique achieves a better performance in terms of the bit error rate (BER) and sum-rate compared to the existing broadcast channel schemes.

A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks.

Interference alignment (IA) is a novel technique that can effectively eliminate the interference and approach the sum capacity of wireless sensor networks (WSNs) when the signal-to-noise ratio (SNR) is high, by casting the desired signal and interference into different signal subspaces. The traditional alternating minimization interference leakage (AMIL) algorithm for IA shows good performance in high SNR regimes, however, the complexity of the AMIL algorithm increases dramatically as the number of users and antennas increases, posing limits to its applications in the practical systems. In this paper, a novel IA algorithm, called directional quartic optimal (DQO) algorithm, is proposed to minimize the interference leakage with rapid convergence and low complexity. The properties of the AMIL algorithm are investigated, and it is discovered that the difference between the two consecutive iteration results of the AMIL algorithm will approximately point to the convergence solution when the precoding and decoding matrices obtained from the intermediate iterations are sufficiently close to their convergence values. Based on this important property, the proposed DQO algorithm employs the line search procedure so that it can converge to the destination directly. In addition, the optimal step size can be determined analytically by optimizing a quartic function. Numerical results show that the proposed DQO algorithm can suppress the interference leakage more rapidly than the traditional AMIL algorithm, and can achieve the same level of sum rate as that of AMIL algorithm with far less iterations and execution time.

Adaptive limited feedback for interference alignment in MIMO interference channels.

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

On the interference alignment with limited feedback for device-to-device networks.

In this paper, we analyze the performance of interference alignment of device-to-device (D2D) uplink underlay cellular networks. By fully considering the impact of imperfect precoding vectors caused by limited feedback, the exact closed-form expressions of average throughput for cellular network and D2D network are derived in terms of transmit power and the number of feedback bits. The accuracy of the average throughput is verified by simulation results. Our analytic results provide great promises to practical system designs.