Mohammed E. Eltayeb

Compressive Sensing for Feedback Reduction in MIMO Broadcast Channels

In multi-antenna broadcast networks, the base stations (BSs) rely on the channel state information (CSI) of the users to perform user scheduling and downlink transmission. However, in networks with large number of users, obtaining CSI from all users is arduous, if not impossible, in practice. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing (CS), which permits the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead. Additionally, assuming noisy CS measurements at the BS, inexpensive ways for improving post-CS detection are explored. The proposed techniques are shown to reduce the feedback overhead, improve CS detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback channels.

Opportunistic beam training with hybrid analog/digital codebooks for mmWave systems

Millimeter wave (mmWave) communication is one solution to provide more spectrum than available at lower carrier frequencies. To provide sufficient link budget, mmWave systems will use beamforming with large antenna arrays at both the transmitter and receiver. Training these large arrays using conventional approaches taken at lower carrier frequencies, however, results in high overhead. In this paper, we propose a beam training algorithm that efficiently designs the beamforming vectors with low training overhead. Exploiting mmWave channel reciprocity, the proposed algorithm relaxes the need for an explicit feedback channel, and opportunistically terminates the training process when a desired quality of service is achieved. To construct the training beamforming vectors, a new multi-resolution codebook is developed for hybrid analog/digital architectures. Simulation results show that the proposed algorithm achieves a comparable rate to that obtained by exhaustive search solutions while requiring lower training overhead when compared to prior work.

Opportunistic Relay Selection With Limited Feedback

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Generally, relay selection algorithms require channel state information (CSI) feedback from all cooperating relays to make a selection decision. This requirement poses two important challenges, which are often neglected in the literature. Firstly, the fed back channel information is usually corrupted by additive noise. Secondly, CSI feedback generates a great deal of feedback overhead (air-time) that could result in significant performance hits. In this paper, we propose a compressive sensing (CS) based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels. The proposed algorithm exploits CS to first obtain the identity of a set of relays with favorable channel conditions. Following that, the CSI of the identified relays is estimated using least squares estimation without any additional feedback. Both single and multiple relay selection cases are considered. After deriving closed-form expressions for the asymptotic end-to-end SNR at the destination and the feedback load for different relaying protocols, we show that CS-based selection drastically reduces the feedback load and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback.

On the Feedback Reduction of Multiuser Relay Networks Using Compressive Sensing

This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory are introduced and their effect on the system performance is analyzed. The problem of joint user identity and signal-to-noise ratio (SNR) estimation at the base-station is casted as a block sparse signal recovery problem in CS. Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated using the best linear unbiased estimator (BLUE). To minimize the effect of feedback noise on the estimated SNRs, a backoff strategy that optimally backsoff on the noisy estimated SNRs is introduced, and the error covariance matrix of the noise after CS recovery is derived. Finally, closed-form expressions for the end-to-end SNRs of the system are derived. Numerical results show that the proposed techniques drastically reduce the feedback air-time and achieve a rate close to that obtained by scheduling techniques that require dedicated error-free feedback from all network users. Key findings of this paper suggest that the choice of half-duplex or full-duplex SNR feedback is dependent on the channel coherence interval, and on low coherence intervals, full-duplex feedback is superior to the interference-free half-duplex feedback.

On the Security of Millimeter Wave Vehicular Communication Systems Using Random Antenna Subsets

Millimeter wave (mmWave) vehicular communication systems have the potential to improve traffic efficiency and safety. Lack of secure communication links, however, may lead to a formidable set of abuses and attacks. To secure communication links, a physical layer precoding technique for mmWave vehicular communication systems is proposed in this paper. The proposed technique exploits the large dimensional antenna arrays available at mmWave systems to produce direction dependent transmission. This results in coherent transmission to the legitimate receiver and artificial noise that jams eavesdroppers with sensitive receivers. Theoretical and numerical results demonstrate the validity and effectiveness of the proposed technique and show that the proposed technique provides high secrecy throughput when compared to conventional array and switched array transmission techniques.

Distributed User Selection in Network MIMO Systems with Limited Feedback

We propose a distributed user selection strategy in a network MIMO setting with M base stations serving K users. Each base station is equipped with L antennas, where LM ≪ K. The conventional selection strategy is based on a well known technique called semi-orthogonal user selection when the zero-forcing beamforming (ZFBF) is adopted. Such technique, however, requires perfect channel state information at the transmitter (CSIT), which might not be available or need large feedback overhead. This paper proposes an alternative distributed user selection technique where each user sets a timer that is inversely proportional to his channel quality indicator (CQI), as a means to reduce the feedback overhead. The proposed strategy allows only the user with the highest CQI to respond with a feedback. Such technique, however, remains collision free only if the transmission time is shorter than the difference between the strongest user timer and the second strongest user timer. To overcome the situation of longer transmission times, the paper proposes another feedback strategy that is based on the theory of compressive sensing, where collision is allowed and all users encode their feedback information and send it back to the base-stations simultaneously. The paper shows that the problem can be formulated as a block sparse recovery problem which is agnostic on the transmission time, which makes it a good alternative to the timer approach when collision is dominant.

Enhancing Secrecy With Multiantenna Transmission in Millimeter Wave Vehicular Communication Systems

Millimeter wave (mmWave) vehicular communication systems will provide an abundance of bandwidth for the exchange of raw sensor data and support driver-assisted and safety-related functionalities. Lack of secure communication links, however, may lead to abuses and attacks that jeopardize the efficiency of transportation systems and the physical safety of drivers. In this paper, we propose two physical layer (PHY) security techniques for vehicular mmWave communication systems. The first technique uses multiple antennas with a single radio-frequency (RF) chain to transmit information symbols to a target receiver and noise-like signals in nonreceiver directions. The second technique uses multiple antennas with a few RF chains to transmit information symbols to a target receiver and opportunistically inject artificial noise in controlled directions, thereby reducing interference in vehicular environments. Theoretical and numerical results show that the proposed techniques provide higher secrecy rate when compared to traditional PHY security techniques that require digital or more complex antenna architectures.

Relay Selection with Limited and Noisy Feedback

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Nonetheless, relay selection algorithms generally require error-free channel state information (CSI) from all cooperating relays. Practically, CSI acquisition generates a great deal of feedback overhead that could result in significant transmission delays. In addition to this, the fed back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. In this paper, we propose a relay selection algorithm that tackles the above challenges. Instead of allocating each relay a dedicated channel for feedback, all relays share a pool of feedback channels. Following that, each relay feeds back its identity only if its effective channel (source-relay-destination) exceeds a threshold. After deriving closed-form expressions for the feedback load and the achievable rate, we show that the proposed algorithm drastically reduces the feedback overhead and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback from all relays.

Downlink scheduling using non-orthogonal uplink beams

Opportunistic schedulers rely on the feedback of the channel state information of users in order to perform user selection and downlink scheduling. This feedback increases with the number of users, and can lead to inefficient use of network resources and scheduling delays. We tackle the problem of feedback design, and propose a novel class of nonorthogonal codes to feed back channel state information. Users with favorable channel conditions simultaneously transmit their channel state information via non-orthogonal beams to the base station. The proposed formulation allows the base station to identify the strong users via a simple correlation process. After deriving the minimum required code length and closed-form expressions for the feedback load and downlink capacity, we show that: the proposed algorithm reduces the feedback load while matching the achievable rate of full feedback algorithms operating over a noiseless feedback channel; and the proposed codes are superior to the Gaussian codes.

Compressive Sensing for Blockage Detection in Vehicular Millimeter Wave Antenna Arrays

The radiation pattern of an antenna array depends on the excitation weights and the geometry of the array. Due to mobility, some vehicular antenna elements might be subjected to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. These particles cause absorption and scattering to the signal incident on the array, and as a result, change the array geometry. This distorts the radiation pattern of the array mostly with an increase in the sidelobe level and decrease in gain. In this paper, we propose a blockage detection technique for millimeter wave vehicular antenna arrays that jointly estimates the locations of the blocked antennas and the attenuation and phase-shifts that result from the suspended particles. The proposed technique does not require the antenna array to be physically removed from the vehicle and permits real-time array diagnosis. Numerical results show that the proposed technique provides satisfactory results in terms of block detection with low detection time provided that the number of blockages is small compared to the array size.