We discuss in this paper the effects of antenna efficiency in the presence of a user and on the performance of a multiple-input multiple-output system. The investigation is carried out in different usage scenarios where the antenna efficiency changes significantly from one scenario to another. The propagation channel is defined to include the 3D antenna electromagnetic properties with the 3GPP spatial channel model. We then investigate at a link level using a Long Term Evolution (LTE) link simulator where the antenna and the user are integrated into the link. The analyses are based on channel correlation, channel power transfer factor, and the link throughput. We show the importance of accurately accounting for the antenna efficiency for realistic MIMO link performance. We also show that accounting for the antenna efficiency is a weighted factor defined by the antenna pattern behavior.

Abstract - A new inter-cell interference aware channel estimation technique for OFDM systems is presented. In the proposed algorithm, user equipment (UE) jointly detects the channel weights of the communication links associated with the serving and the interfering cells. Performance benefits of the proposed method are demonstrated by considering the OFDMA downlink as defined in 3GPP Long Term Evolution (LTE). The raw bit error rate results with the new estimation technique and the well-known least squares (LS) channel estimator are compared in the presence of one dominant inter-cell interferer.

We investigate how accurate Kronecker model is compared to Spatial Channel Model (SCM) for MIMO link layer simulations. The simulation results show that Kronecker model is a good approximation for MIMO in link layer simulations compared to SCM.

In this paper, a new Selective Mapping (SLM) technique is introduced for a Multiple-Input Multiple-Output (MIMO) broadcast (or MIMO point-to-point) system with channel inversion. We present a unified framework which links the problem of minimizing the average transmit energy from the constellation point of view, to the sum-rate maximization problem from the capacity viewpoint. First, we consider the point-to-point setup and derive the average transmit energy that can asymptotically be achieved by the proposed SLM technique. It is established that SLM achieves the minimum theoretical average transmit energy achievable by constellation shaping. Then, using the same idea, the average transmit energy in a broadcast system is derived and the relation between this value and the transmission rate is established. Finally, it is shown that the proposed SLM method can achieve the sum-capacity of the MIMO broadcast (or capacity of a MIMO point-to-point) channel at high SNR values.

For a wide class of multiuser systems, a subset of capacity region which includes the corner points and the sum-capacity facet has a special structure known as polymatroid. Multiple-access channels with fixed input distributions and multiple-antenna broadcast channels are examples of such systems. Any interior point of the sum-capacity facet can be achieved by time-sharing among corner points or by an alternative method known as rate-splitting. The main purpose of this paper is to find a point on the sum-capacity facet which satisfies a notion of fairness among the active users. This problem is addressed in two cases: (i) where the complexity of achieving interior points is not feasible, and (ii) where the complexity of achieving interior points is feasible. For the first case, the corner point for which the minimum rate of the active users is maximized is desired. A simple greedy algorithm is introduced to find such an optimum corner point. In addition, it is shown for single-antenna Gaussian multiple-access channels, the resulting corner point is leximin maximal with respect to the set of the corner points. For the second case, the properties of the unique leximin maximal rate vector with respect to the polymatroid are reviewed. It is shown that the problems of deriving the time-sharing coefficients or rate-splitting scheme to attain the leximin maximal vector can be solved by decomposing the problem into some lower dimensional subproblems. In addition, a fast algorithm to compute the time-sharing coefficients to attain a general point on the sum-capacity facet is presented.

In multiple-input multiple-output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an$N$-dimensional complex space. In general, this problem is known to be NP-hard. In this paper, a quasi-ML algorithm based on semi-definite programming (SDP) is proposed. We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models. The proposed relaxation models are also used for soft output decoding in MIMO systems.

A new viewpoint for adopting the lattice reduction in communication over multiple-input multiple-output (MIMO) broadcast channels is introduced. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. The new viewpoint helps us to generalize the idea of lattice-reduction-aided (LRA) preceding for the case of unequal-rate transmission, and obtain analytic results for the asymptotic behavior (signal-to-noise ratio (SNR) rarr infin) of the symbol error rate for the LRA precoding and the perturbation technique. Also, the outage probability for both cases of fixed-rate users and fixed sum rate is analyzed. It is shown that the LRA method, using the Lenstra-Lenstra-Lovasz (LLL) algorithm, achieves the optimum asymptotic slope of symbol error rate (called the precoding diversity).

Diversity order is an important measure for the performance of communication systems over multiple-input-multiple-output (MIMO) fading channels. In this correspondence, we prove that in MIMO multiple- access systems (or MIMO point-to-point systems with V-BLAST transmission), lattice-reduction-aided decoding achieves the maximum receive diversity (which is equal to the number of receive antennas). Also, we prove that the naive lattice decoding (which discards the out-of-region decoded points) achieves the maximum diversity.

For a wide class of multiuser systems, a subset of capacity region which includes the corner points and the sum-capacity facet has a special structure known as polymatroid. Any interior point of the sum-capacity facet can be achieved by time-sharing among corner points or by an alternative method known as rate-splitting. The main purpose of this paper is to find a point on the sum-capacity facet which satisfies a notion of fairness among active users. In one case, the corner point for which the minimum rate of the active users is maximized (max-min corner point) is computed for signaling. In another case, the polymatroid properties are exploited to locate a rate-vector on the sum-capacity facet which is optimally fair in the sense that the minimum rate among all users is maximized (max-min rate). It is shown that the problems of deriving the time-sharing coefficients or rate-splitting scheme can be solved by decomposing the problem to some lower-dimensional subproblems. In addition, a fast algorithm to compute the time-sharing coefficients to attain a general point on the sum-capacity facet is proposed

In this paper, the problem of reducing the peak-to-average-power ratio (PAPR) in an orthogonal frequency-division multiplexing system is considered. We design a cubic constellation, called the Hadamard constellation, whose boundary is along the bases defined by the Hadamard matrix in the transform domain. Then, we further reduce the PAPR by applying the selective-mapping technique. The encoding method, following the method introduced in the work of Kwok, is derived from a decomposition known as the Smith normal form. This new technique offers a PAPR that is significantly lower than those of the best-known techniques without any loss in terms of energy and/or spectral efficiency, and without any side information being transmitted. Moreover, it has a low computational complexity.

In multi-input multi-output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on semi-definite programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models

Diversity order is an important measure for the performance of different communication systems over MIMO fading channels. In this paper, we define the preceding diversity for the fixed-rate MIMO broadcast systems and we prove that in these systems, lattice-reduction-aided preceding achieves the preceding diversity. Also, we prove that lattice-reduction-aided decoding achieves the receive diversity in MIMO point-to-point and multiple-access systems