The next-generation cellular networks are facing several challenges. Precisely, according to recent statistics, by 2021, video traffic will represent 82% of the IP traffic. This demand increase will affect the users' quality-of-service (QoS). Accordingly, integrating appropriate distributed source coding (DSC) frameworks for mobile data traffic is crucially needed. Wyner-Ziv coding is considered a great solution to the high demand for video traffic. However, in LTE-A, the current standard of quality-of-service class identifier (QCI) gives a higher priority to the best-effort traffic, i.e., FTP and the rate-constrained traffic, i.e., VoIP. This current prioritization may not be suitable for the growing demands for the delay-constrained traffic, i.e., video traffic. Accordingly, in this paper, we investigate the effect of giving higher priority to the video-traffic on the QoS. To address this problem, we propose a QoS-aware downlink scheduler in a single-cell LTE network. In the proposed packet scheduler, we use a utility-based scheduling approach to study the effect of adopting Wyner-Ziv coding with prioritizing video traffic over other traffic types. Numerical results confirm that when using the Wyner-Ziv coding, changing the traffic priority to grant the video traffic the highest, will not impact the system's performance compared to other existing schemes.

The power line is inherently a broadcast medium when used for data transmission, since the electricity grid has no well defined boundaries for communication signals. This broadcast nature of the channel leads to mutual interference of communication signals emitted in relative proximity. To overcome the rate-limiting effect of this interference, recently the concept of interference alignment (IA) has been applied to power line communications (PLC). In particular, the benefits of IA have been shown for transmission in multi-input multi-output (MIMO) PLC systems. However, the results were obtained under the assumption of independent noise at the different ports of the PLC receiver. Unlike for most wireless communications scenarios for which IA has usually been studied, this assumption is often not valid for MIMO PLC. In particular, due to the dominance of noise signals induced and broadcast from grid-connected devices as well as the signal cross-coupling in PLC networks, noise is often found to be spatially correlated. Therefore, in this paper we explore the performance of IA for MIMO PLC in the presence of correlated noise obtained from indoor measurements. To this end, we adopt the maximum signal- to-interference-plus-noise ratio IA algorithm, whose formulation includes the noise statistics. Numerical results quantify the effect of noise correlation on the achievable sum-rate for MIMO PLC with IA.

In this paper, we study the mutual interference in the context of a hybrid interweave-underlay cognitive communication where transmission constraints from both primary and secondary networks are considered. The transmission of a secondary user in such a network is generally constrained by the primary network so as to avoid harmful interference to the primary receiver. In addition, we consider a constraint from the secondary network to avoid harmful interference to other secondary receivers. To this end, the transmission probability of a secondary user is derived from the joint density function of the distances from secondary users to the primary receiver and that of between secondary users. Inspired by inherent benefits of an interweave-underlay hybrid approach, we further consider the impact of spectrum sensing on the transmission probability of the secondary users. The derived expression allows us to analyze the interdependency of protection margins that may be enforced by the primary and secondary networks. Relying on this analysis, we then derive closed-form expressions for expected aggregated interference to the primary and secondary receivers. Furthermore, we propose a power control approach enabled by an in-band signaling link to minimize the mutual interference to the receivers in the network. In order to show the effectiveness of the proposed interference modeling, we simulate a cognitive network with such constraints from both networks and evaluate the aggregated interference experienced by the receivers. Finally, we validate our interference model by comparing theoretical and simulated aggregated interference experienced by the receivers.

We consider optimal linear precoder and decoder designs in a multi-cell multiple-input multiple-output system, where base stations and mobile users are both operating in full-duplex (FD) mode. Existing works on FD cellular systems focus on the maximization of overall throughput, which can result in unfairness between uplink and downlink channels depending on the self-interference power and inter-user interference levels. Therefore, to introduce fairness, in this paper, we consider the transmit and receive beamforming designs that maximize the harmonic-sum of signal-to-interference-plus-noise ratios (SINRs) in the uplink and downlink channels. We propose a low-complexity alternating optimization algorithm which converges to a stationary point. Moreover, in order to address practical system design aspects, we consider the transceiver design that enforces robustness against imperfect channel state information (CSI) while providing fair performance among the users. To this end, we formulate an optimization problem that maximizes the worst case SINR among all users under norm-bounded CSI errors. We devise a low-complexity iterative algorithm based on alternating optimization and semidefinite relaxation techniques. Numerical results verify the advantages of incorporating FD mode into cellular systems, and practical issues, such as CSI uncertainty and fairness performance.

In this paper, we consider mean squared error (MSE)-based robust transceiver optimization for multi-user multi-input multi-out (MU-MIMO) cellular network that exploits interference alignment (IA) for its downlink communication. First, we consider the conventional sum-MSE minimization problem, where the MSE is calculated at user equipments (UEs). Second, we consider a different approach where the MSE can be calculated at base stations (BSs) i.e., leakage-based MSE from each BS. Different from conventional per-base station power constraint (PBPC), we assume practical per-antenna power constraint (PAPC) due to linearity of the power amplifier that feeds each transmit antenna at the BSs. To this end, we derive robust precoders and receive filters for these two design objectives under statistical channel state information (CSI) uncertainty. Simulation results suggest that our robust designs offer a good performance, showing resilience against CSI uncertainty. In addition, the sum-rate performances for designs with PAPC and PBPC are compared. It is observed that the sum-rate loss due to PAPC is relatively small for the robust designs.

In this paper, we study robust transceiver optimization for device-to-device (D2D) communications that aims for signal-to-interference-plus-noise-ratio (SINR) fairness among D2D users. We assume that the multi-antenna D2D users in the cellular network employ interference alignment (IA) for their communication. In addition to an interference power constraint that is set by the primary network (i.e., macrocell) in the underlay cognitive transmission, we also consider channel state information (CSI) imperfectness to obtain a robust transceiver design for the D2D users. Due to the non-convexity of the design problem, we resort to an alternating minimization technique. Numerical simulations demonstrate the performance of the proposed transceiver compared to the benchmark case of an IA system without primary network/macrocell (non-cognitive). It is observed that at low SNR and high CSI error with relaxed interference power constraint, the worst-case stream data rate of the D2D users are close to that of the users in non-cognitive IA system but performance degrades significantly with stringent interference power constraint.

In a multi-input multi-output (MIMO) interference network, the maximum achievable signal-to-interference-plus-noise ratio (SINR) determines the level of quality of service (QoS) that can be supported by each stream. In this paper, we study the problem of maximizing the minimum SINR of independent parallel data streams across all users in a MIMO interference network that employs interference alignment (IA) in the presence of channel state information (CSI) uncertainty. To this end, we propose a low-complexity robust SINR fairness algorithm based on the power allocation across data streams. Building on the conventional IA design, we provide a closed-form solution for the power allocation problem. It is observed that the proposed power allocation (PA)-based robust design outperforms the conventional robust design, when the channel changes drastically and the CSI uncertainty cannot be reliably bounded. Furthermore, the PA-based robust design provides significant performance gain over the benchmark Max-SINR algorithm.

Equalization of fast time-varying channels is impacted by short coherence time and deep signal fading, especially in vehicle-to-vehicle communications due to the high mobility of user terminals. Furthermore, the stringent latency requirement of safety applications, such as collision avoidance, cannot tolerate high-complexity operations and long processing time. In order to achieve both requirements of equalization accuracy and short latency for fast varying channels, we propose a new model-based time-domain equalizer. In this equalizer, estimation and equalization are performed in two parallel parts to shorten the processing time. In main path, data symbols pass through an equalizer preset with the up-to-date channel impulse response. Since the channel variation model remains invariable for sufficiently long time, the current channel is estimated in parallel path from a number of past channel impulse responses to improve the accuracy. However, the channel variation during the long processing time of estimation leads to equalization error. Therefore, a predictor is used to update the channel response related to the processing delay, and perform the channel estimation beyond the channel coherence time. Thus, high accuracy and delay-free equalization can be achieved through this parallel structure.

Mutual interference plays decisive role in successful operation of a cognitive radio (CR) network. In order to evaluate the interference experienced by a primary user, we have considered a realistic communication scenario where the transmission of any secondary user is not only constrained by the primary network but also constrained from the secondary network itself so that harmful interference to other secondary users can be avoided. The transmission probability for each secondary user has been derived using joint density function of the distance from secondary user to the primary receiver and the distance between adjacent secondary users. Based on this analysis, a closed-form expression for expected mutual interference experienced by a primary receiver is derived. In addition, a cognitive network is simulated to evaluate the aggregated interference experienced by the primary receiver along with constraints from both primary and secondary networks. The proposed interference modeling is validated by comparing theoretical and simulated results of aggregated interference experienced by the primary receiver.

In order to improve the transmit power efficiency at the Mobile Station (MS), the transmit power is usually adjusted based on feedback information from the Base Station (BS) or Channel State Information (CSI) estimated at the MS. In fast-varying channels, because of the propagation and estimation delays, and the short channel coherence time, both the feedback information and the estimated CSI may become outdated at the transmit instant, leading to a reduction in power control performance. In this paper, a new channel prediction-based adaptive power control technique is proposed for uplink transmission in Time Division Duplex (TDD) Orthogonal Frequency Division Multiplexing (OFDM) systems. Based on the predicted Channel Impulse Responses (CIRs) provided by a cluster-based time- domain channel predictor, the transmit power is allocated to each OFDM subcarrier and then, a global gain is applied to compensate for the propagation path loss. In doing this, the power control process does not rely on the feedback information from the BS nor the estimated CSI at the MS and thus, the system responsiveness, adaptivity, and power savings are improved.

The ever-increasing demand for precious radio spectrum along with the inefficient usage of licensed band has led to the advent of the cognitive radio (CR) technology, which aims to provide opportunistic spectrum usage to unlicensed users and thus lead to the co-existence and interference control problem among heterogeneous systems. In this paper, an interference minimization and subcarrier power allocation approach for orthogonal frequency division multiplexing (OFDM)-based cognitive network is proposed. A transmission power negotiation signaling between CR transmitter and receiver is established through the use of encoded cyclic prefix (CP). Therefore, mutual interference to primary and other cognitive users can be minimized with reduced unnecessary transmission power. In addition, system performance of the receiver can be guaranteed in the process of mutual interference minimization. Beside this, subcarrier power allocation profile can be chosen from a set of predefined profiles and can be sent at the same time without additional signaling link and extra delay to the network. The transceiver structure and control signal encoding and decoding algorithm are investigated. The performance of the proposed signaling links are also analyzed and evaluated through simulations in different channel scenarios. In addition, interference minimization technique is validated through the simulation of probability density function (PDF) and then in a cognitive radio network with randomly distributed nodes to assess the overall perceived interference.

An adaptive Orthogonal Frequency Division Multiplexing (OFDM) system, having precoded cyclic prefix (PCP) was proposed earlier as a way to carry the control signaling parameters to provide much needed flexible transmission techniques in cognitive radio (CR) communications. The ultimate goal of the PCP-OFDM system is to achieve concurrent transmission of CR control signaling parameters with data information. Therefore, hardware and computational efficient demodulation of PCP is of huge importance to recover the signaling parameters and for the design of fair spectrum sharing mechanism in cognitive radio scenario. But due to the hardware and computational complexity of the conventional optimal matched filter, recently a demodulator was proposed in paper which has reduced complexity than the conventional approach. However, the complexity is still high which is often very difficult to implement and also not cost-effective. In this paper, we propose a low complexity demodulator for the efficient demodulation of the signaling information carried by the PCPs. Design of this demodulator is presented and performance is evaluated through the simulations in different channel scenarios. Along with a peak combining technique, impact of the multipath impairment can be mitigated to a reasonable extent. More importantly, the hardware and computational complexity is reduced substantially compared with the other techniques proposed earlier. It is also found from the numerical simulation results that this demodulation scheme provides the same performance as the conventional optimal matched filter, with significantly reduced hardware and computational complexity, therefore reducing the overall complexity and cost of the PCP-OFDM receiver.

An adaptive orthogonal frequency division multiplexing (OFDM) system, with a preceded cyclic prefix (PCP) as a signaling link was proposed earlier to address the recent need of flexible transmission techniques in cognitive radio (CR) communications. The flexibility of the PCP-OFDM system relies on the concurrent transmission of OFDM signal and PCP signaling which represents OFDM system parameters including bandwidth, modulation, coding schemes etc. Efficient demodulation of PCP signaling is therefore of great importance for OFDM data recovery and reduce the delay from the system adaption. In this paper, we propose to use a three-stage demodulator to recover the signaling information carried by the PCPs. Design of this three-stage demodulator are analyzed and performance is evaluated through the simulations in different channel conditions. In addition, with proposed peak combining technique, impact of the multipath impairment is mitigated to a great extent. It is observed from the simulation results that this signaling demodulation scheme provides the same performance as the conventional optimal matched filter but with significantly reduced hardware and computational complexity.