Muhammad Fainan Hanif

A Minorization-Maximization Method for Optimizing Sum Rate in the Downlink of Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) systems have the potential to deliver higher system throughput, compared with contemporary orthogonal multiple access techniques. For a linearly precoded multiple-input single-output (MISO) system, we study the downlink sum rate maximization problem, when the NOMA principle is applied. Being a non-convex and intractable optimization problem, we resort to approximate it with a minorization-maximization algorithm (MMA), which is a widely used tool in statistics. In each step of the MMA, we solve a second-order cone program, such that the feasibility set in each step contains that of the previous one, and is always guaranteed to be a subset of the feasibility set of the original problem. It should be noted that the algorithm takes a few iterations to converge. Furthermore, we study the conditions under which the achievable rates maximization can be further simplified to a low complexity design problem, and we compute the probability of occurrence of this event. Numerical examples are conducted to show a comparison of the proposed approach against conventional multiple access systems.

Fast Converging Algorithm for Weighted Sum Rate Maximization in Multicell MISO Downlink

The problem of maximizing weighted sum rates in the downlink of a multicell environment is of considerable interest. Unfortunately, this problem is known to be NP-hard. For the case of multi-antenna base stations and single antenna mobile terminals, we devise a low complexity, fast and provably convergent algorithm that locally optimizes the weighted sum rate in the downlink of the system. In particular, we derive an iterative second-order cone program formulation of the weighted sum rate maximization problem. The algorithm converges to a local optimum within a few iterations. Superior performance of the proposed approach is established by numerically comparing it to other known solutions.

A Conic Quadratic Programming Approach to Physical Layer Multicasting for Large-Scale Antenna Arrays

We investigate the problem of downlink physical layer multicasting that aims at minimizing the transmit power with a massive antenna array installed at the transmitter site. We take a solution based on semidefinite relaxation (SDR) as our benchmark. It is shown that instead of working on the semidefinite program (SDP) naturally produced by the SDR, the dual counterpart of the same problem may provide a more efficient numerical implementation. Later, by using a successive convex approximation strategy, we arrive at a provably convergent iterative second-order cone programming (SOCP) solution. Our thorough numerical investigations report that the newly proposed SOCP solution offers improved power efficiency and a massively reduced computational complexity. Therefore, the SOCP solution is seen as a suitable candidate for obtaining beamformers that minimize transmit power, especially, when a very large number of antennas is used at the transmitter.

Interference and Deployment Issues for Cognitive Radio Systems in Shadowing Environments

In this paper we describe a model for calculating the aggregate interference encountered by primary receivers in the presence of randomly placed cognitive radios (CRs). We show that incorporating the impact of distance attenuation and lognormal fading on each constituent interferer in the aggregate, leads to a composite interference that cannot be satisfactorily modeled by a lognormal. Using the interference statistics we determine a number of key parameters needed for the deployment of CRs. Examples of these are the exclusion zone radius, needed to protect the primary receiver under different types of fading environments and acceptable interference levels, and the numbers of CRs that can be deployed. We further show that if the CRs have apriori knowledge of the radio environment map (REM), then a much larger number of CRs can be deployed especially in a high density environment. Given REM information, we also look at the CR numbers achieved by two different types of techniques to process the scheduling information.

MIMO Cognitive Radios with Antenna Selection

In this paper, we propose two solutions to the problem of joint transmit-receive antenna selection in a multiple-input multiple-output (MIMO) cognitive radio (CR) system. Our objective is to maximize CR data rates and satisfy interference constraints at the primary user (PU) receiver(s). In the first we approximate the original non-convex optimization problem using an iterative approach solving a series of smaller convex problems. Second we present a novel, norm-based transmit receive antenna selection technique that simultaneously improves throughput while maintaining the PU interference constraints. We show that this approach yields near optimal results with massive complexity reductions. We make a performance comparison between the proposed approaches and the optimal exhaustive search approach. We provide an analysis of the exhaustive search and relate selection gains to system parameters such as the shadow fading standard deviation, the path loss exponent and the number of PUs per square kilometer. Our results establish that antenna selection is a promising option for future MIMO CR devices in sparse PU environments.

Efficient Solutions for Weighted Sum Rate Maximization in Multicellular Networks With Channel Uncertainties

The important problem of weighted sum rate maximization (WSRM) in a multicellular environment is intrinsically sensitive to channel estimation errors. In this paper, we study ways to maximize the weighted sum rate in a linearly precoded multicellular downlink system where the receivers are equipped with a single antenna. With perfect channel information available at the base stations, we first present a novel fast converging algorithm that solves the WSRM problem. Then, the assumption is relaxed to the case where the error vectors in the channel estimates are assumed to lie in an uncertainty set formed by the intersection of finite ellipsoids. As our main contributions, we present two procedures to solve the intractable nonconvex robust designs based on the worst case principle. The proposed iterative algorithms solve semidefinite programs in each of their steps and provably converge to a locally optimal solution of the robust WSRM problem. The proposed solutions are numerically compared against each other and known approaches in the literature to ascertain their robustness towards channel estimation imperfections. The results clearly indicate the performance gain compared to the case when channel uncertainties are ignored in the design process. For certain scenarios, we also quantify the gap between the proposed approximations and exact solutions.

On Linear Precoding Strategies for Secrecy Rate Maximization in Multiuser Multiantenna Wireless Networks

Revived interest in physical layer security has led to a cascade of information theoretic results for various system topologies under different constraints. In the present paper, we provide practically oriented solutions to the problem of maximizing achievable secrecy rates in an environment consisting of multiple legitimate and eavesdropping radio nodes. By assuming “genie” aided perfect channel state information (CSI) feedback for both types of nodes, we first study two scenarios of interest. When independent messages are intended for all legitimate users (called “broadcast” mode), provably convergent second-order cone programming (SOCP)-based iterative procedure is used for designing secrecy rate maximizing beamformers. In the same manner, when a common message is intended only for legitimate nodes (dubbed “multicast” mode), SOCP-based design is proposed for obtaining linear precoders that maximize the achievable secrecy rate. Subsequently, we leverage the analysis to the more real-world scenario, where the CSI of the malicious nodes has to be somehow estimated and that of the legitimate users is corrupted with unavoidable errors. For this case, we devise provably convergent iterative semidefinite programming (SDP) procedures that maximize the achievable secrecy rates for both the beamforming-based broadcast and the linearly precoded multicast modes. Finally, numerical results are reported that evaluate the performance of the proposed solutions as a function of different system parameters. The results presented in the paper are demonstrated to outperform the ones based on interference alignment strategies. We also ascertain superior performance of the proposed schemes in the realms of real world.

On MIMO Cognitive Radios with Antenna Selection

With the ever increasing interest in multiple-input multiple-output (MIMO) cognitive radio (CR) systems, reducing the costs associated with RF-chains at the radio front end becomes a very important factor. In this paper, we propose two solutions to the problem of joint transmit-receive antenna selection with the objective of maximizing data rates and satisfying interference constraints at the primary user (PU) receiver. In the first method we approximate the original non-convex optimization problem with an iterative way of solving a series of smaller convex problems. Then we present a novel, norm-based transmit receive antenna selection technique that simultaneously improves throughput while maintaining the PU interference constraints. We show that this simple approach yields near optimal results with massive complexity reductions. In addition to making a performance comparison between the proposed approaches and the optimal exhaustive search approach, we establish that antenna selection is a promising option for future MIMO CR devices.

Computationally Efficient Robust Beamforming for SINR Balancing in Multicell Downlink With Applications to Large Antenna Array Systems

We address the problem of the downlink beamformer design for signal-to-interference-plus-noise ratio balancing in a multiuser multicell environment with imperfectly estimated channels at base stations. We first present a semidefinite program (SDP)-based approximate solution to the problem. Then, as our main contribution, by exploiting some properties of the robust counterpart of the optimization problem, we arrive at a second-order cone program (SOCP)-based approximation of the balancing problem. The advantages of the proposed SOCP-based design are twofold. First, it greatly reduces the computational complexity compared to the SDP-based method. Second, it applies to a wide range of uncertainty models. As a case study, we investigate the performance of proposed formulations when the base station is equipped with a massive antenna array. Numerical experiments are carried out to confirm that the proposed robust designs achieve favorable results in scenarios of practical interest.

Performance of Cognitive Radio Systems with Imperfect Radio Environment Map Information

In this paper we describe the effect of imperfections in the radio environment map (REM) information on the performance of cognitive radio (CR) systems. Via simulations we explore the relationship between the required precision of the REM and various channel/system properties. For example, the degree of spatial correlation in the shadow fading is a key factor as is the interference constraint employed by the primary user. Based on the CR interferers obtained from the simulations, we characterize the temporal behavior of such systems by computing the level crossing rates (LCRs) of the cumulative interference represented by these CRs. This evaluates the effect of short term fluctuations above acceptable interference levels due to the fast fading. We derive analytical formulae for the LCRs in Rayleigh and Rician fast fading conditions. The analytical results are verified by Monte Carlo simulations.