Multiuser scheduling enables users to share the same time and frequency resources while exploiting spatial diversity through the use of multiple antennas. In this paper, we propose a machine learning (ML) approach that decides on multiuser scheduling through solving a system capacity optimization problem. More specifically, we use a support vector machine (SVM). The proposed algorithm takes as an input the signal to noise ratio (SNR) and uplink channel information of a predetermined set of users. The output is a decision as to which users, if any, can be scheduled in the same time slot and frequency band. We show that the resulting system capacity is comparable to the optimal capacity obtained through exhaustive search, with significantly lower algorithm complexity. Moreover, building on the crucial importance of feature-engineering in ML models and capitalizing on the domain-expert knowledge of our problem, we work on tailoring the information available at the scheduler to further enhance the performance of our proposed approach.

In adaptive diffusion networks, one of the main challenges is the large volume of data exchange among nodes needed to arrive at a collective decision. In this study, a new model for adaptive diffusion networks is proposed which offers a tradeoff between the mean-square error performance of the system and the volume of data exchanged among network nodes while preserving the network convergence rate. Study of the mean-square stability of the network under the proposed algorithms is provided. Also, a study of the mean-error dynamic behaviour of the network is carried out. A closed-form expression for the overall network steady-state means-square error is derived and verified against simulated data. The proposed algorithm is applied to a cellular network location estimation problem, and delivers good performance even under 75% reduction in data exchange volume.

Massive multiple-input multiple-output (MIMO) systems can achieve high data rates due to the large number of antennas at the base station when it serves a small number of users. However, deploying massive MIMO in current mobile networks faces a lot of practical issues such as limited physical space and power consumption. Packing large number of antennas in a limited physical space makes spatial correlation between the base station antennas inevitable. In this paper, we study the effect of correlation and we investigate the effectiveness of increasing the number of antennas given limited physical space. Hybrid analog and digital MIMO decoding is adopted to exploit the large array gain at a lower cost and power consumption. Also, different antenna-array structures are investigated and compared in terms of power consumption versus performance.

Coordinated multipoint (CoMP) transmission-reception systems promise an enhanced cell-edge user downlink throughput, specially in time division duplexing (TDD) systems. Radio frequency (RF) calibration is necessary for proper CoMP operation. In this paper, we study the performance of different CoMP schemes and different precoders under RF mismatch calibration errors. Numerical results are provided which show the best CoMP mode candidates given different SNR and calibration error levels.

In integrated mobile satellite systems (MSSs) with ancillary terrestrial component (ATC), seamless handoff techniques will be fundamental for allowing users to switch between the space segment and the terrestrial component. On the other hand, introducing multiple-input multiple-output (MIMO) antenna technology in next-generation MSS-ATC systems promises the well-known advantages related to the MIMO implementation. For performing handoff analysis, the single-input single-output (SISO) channel model equivalent to the MIMO model needs to be developed, which is challenging in MSS-ATC networks because of the high complexity of the satellite channel, and the vast difference in the nature of the MSS and the ATC links. This challenge is tackled in this paper, where an optimal user-driven handoff algorithm for MSS-ATC systems is presented, with both types of links implementing MIMO. Furthermore, for a better signal prediction, Kalman filtering is proposed, which significantly increases the performance. Notably, the application of multi-antenna technology in MSS-ATC systems reveals significant gains in terms of handoff performance.

This paper proposes an enhanced pilot-aided channel estimation algorithm for the second generation of the digital video broadcast for terrestrial (DVB-T2) standard. Two channel estimation techniques are discussed, the first is based on the 3-points averaging technique, while the second is based on studying the frequency domain pattern of the channel response. The second technique leads to a 0.5 dB reduction in the SNR needed to guarantee a specific bit error rate (BER) requirement when compared to the conventional estimation technique recommended by the DVB-T2 implementation guide lines.

Rotated constellation with Q-Delay (RQD) is an innovative scheme that appeared to give better performance compared to the traditional unrotated modulation. The bit error rate (BER) decreases due to the RQD scheme signal space diversity. The authors presented four novel computationally efficient demodulation schemes with hybrid soft-hard outputs in [1] that took advantage of the unique projections the rotated cells have in the RQD system. The Coded Modulation (CM) system can utilize the hybrid computationally efficient demodulation schemes for obtaining better results in terms of BER and computation complexity. One of the hybrid schemes was tested with Low Density Parity Check (LDPC) coder/decoder through two proposed Hard to Soft Assigning algorithms (HSA). These algorithms assign estimated Log-Likelihood Ratio (LLR) values to the hard mapped bits, so that the LDPC decoder receives an all-soft stream. Numerical results show that HSA algorithms give better performance than the original all-soft stream at a moderate SNR range.

Communication regulatory bodies in many parts of the world have recently granted satellite operators the right to extend their networks by adding ground segments. This has opened the door, for the first time, to truly ubiquitous satellite coverage, thus revolutionizing the use of satellites in personal communications. In this article, we review personal satellite communication systems. Background on the evolution of satellite communications is given along with the basics of satellite systems. This is followed by a comprehensive discussion of the current challenges facing the future of personal satellite communication systems and some of the proposed solutions.

This paper presents a locally optimal handoff algorithm for integrated satellite/ground communication systems. We derive the handoff decision function and present the results in the form of tradeoff curves between the number of handoffs and the number of link degradation events in a given distance covered by the mobile user. This is a practical receiver-controlled handoff algorithm that optimizes the handoff process from a user perspective based on the received signal strength rather than from a network perspective.

Rotated constellation with Q-delay (RQD) is a new modulation scheme adopted by some wireless standards including the second generation of the digital video broadcasting for terrestrial (DVB-T2) system. This scheme offers significant improvement over unrotated modulation in terms of bit error rate (BER) performance due to its signal space diversity. In this paper, we present four novel computationally efficient demodulation schemes with hybrid soft-hard outputs used in hard demapping of data. The main idea is to take advantage of the unique projections the rotated cells have in the RQD system. The proposed schemes reduce the number of log likelihood ratio (LLR) computations. One proposed scheme gives the same BER performance as traditional LLR while reducing the computational complexity by 34% at SNR = 20 dB. Another scheme reduces the computations by 25% independent of the SNR level. The other two schemes offer a tradeoff between computational complexity and power efficiency.

In MIMO-OFDM multiuser systems, user scheduling is employed as a means of multiple access. In a downlink scenario, users that share the same subcarriers of an OFDM symbol are separated through precoding in order to achieve space division multiple access (SDMA). User scheduling techniques rely on channel knowledge at the transmitter, namely, the so-called channel quality indicator (CQI). In this paper, we implement a leakage-based precoding algorithm whose purpose is twofold. First, it is used to compute a reliable CQI based on a group of precoding vectors that are adapted to the channel. Then, it implements user scheduling through using the optimum vectors for precoding, thus minimizing interference among users. We also introduce the concept of resource block size adaptivity. The resource block (RB) is defined as the least unit in an OFDM symbol that a user can be assigned to. We propose a variable RB size that adapts to the channel conditions.

In downlink multi-user multiple-input multiple-output (MIMO) transmissions, several precoding schemes have been proposed to decrease interference among users. Notable among these precoding schemes is one that uses the signal-to-leakage-plus-noise ratio (SLNR) as an optimization criterion. In this paper, leveraging the efficiency of the SLNR optimization, we generalize this precoding scheme to MIMO orthogonal frequency division multiplexing (OFDM) multi-user systems where the OFDM is used to overcome the inter-symbol-interference (ISI) introduced by multipath channels. We also introduce a channel compensation technique that reconstructs the channel at the transmitter for every time instant given a significantly lower channel feedback rate by the receiver.

Future mobile communication systems aim to provide extremely high speed data transmission, especially in the downlink. Broadband orthogonal frequency code division multiple access (OFCDMA) with two-dimensional (2D) time and frequency domain spreading is becoming a very promising technique for high speed wireless communications due to its advantages over multi-carrier code division multiple access (MC-CDMA), direct sequence CDMA (DS-CDMA) and orthogonal frequency division multiplexing (OFDM). This paper presents a comparison, through simulation, between the performance of OFCDMA and MC-CDMA systems operating under the same condition. The paper also explains the basic structure of multi-carrier direct sequence CDMA (MC-DS-CDMA) technique, its advantages and OFCDMA system structure. It is shown that OFCDMA is superior to the above mentioned systems.

In this paper, we investigate a semi-blind maximum a posteriori probability-based channel estimator for Alamouti coded OFDM system over time varying Rayleigh channels. This channel estimation is used to handle rapid variations of channel within a transmission block. With the estimate of the channel matrix for the Alamouti symbol interval, a zero-forcing (ZF) receiver can be applied to detect this symbol. Linear and Minimum Mean Square Error (MMSE) decoding algorithms are also introduced in this paper. These receivers take into consideration the variation of channel over two consecutive OFDM symbols, i.e., over Alamouti symbol. The performance of this estimator is compared with Least Square (LS) channel estimator via simulation. Moreover it is shown that using MMSE as a decoding algorithm provides better performance than conventional, linear or ZF decoding methods.

In multiuser MIMO downlink communications, it is necessary to design precoding schemes that are able to suppress co-channel interference. This paper proposes designing precoders by maximizing the so-called signal-to-leakage-and-noise ratio (SLNR) for all users simultaneously. The presentation considers communications with both single- and multi-stream cases, as well as MIMO systems that employ Alamouti coding. The effect of channel estimation errors on system performance is also studied. Compared with zero-forcing solutions, the proposed method does not impose a condition on the relation between the number of transmit and receive antennas, and it also avoids noise enhancement. Simulations illustrate the performance of the scheme

The paper develops a dynamic antenna scheduling strategy for downlink MIMO communications, where a subset of the receive antennas at certain users is selectively disabled. The proposed method improves the signal-to-leakage-plus-noise (SLNR) ratio performance of the system and it relaxes the condition on the number of transmit-receive antennas in comparison to traditional zero-forcing and time-scheduling strategies. The largest value that the SLNR can achieve is shown to be equal to the maximum eigenvalue of a certain random matrix combination, and the probability distribution of this eigenvalue is characterized in terms of a Whittaker function. The result shows that increasing the number of antennas at some users can degrade the SLNR performance at other users. This fact is used to propose an antenna scheduling scheme that leads to improvement in terms of SINR outage probabilities

The paper develops a dynamic antenna scheduling strategy for downlink MIMO communications, where the transmitted signal for each user is beamformed towards a selected subset of receive antennas at this user. The proposed method removes the condition on the number of transmit-receive antennas in comparison to traditional zero-forcing and time-scheduling strategies. By characterizing the probability distribution of the so-called signal-to-leakage-plus-noise (SLNR) ratio, we show that there is an optimal set of receive antennas that maximizes the system performance for each channel realization. This fact is used to propose an antenna scheduling scheme that leads to improvements in terms of SINR outage probabilities.