Chu Zheng, Hao Wanming, Xiao Pei, Zhou Fuhui, Mi De, Zhu Zhengyu, Leung Victor C.M. (2018) Energy Efficient Hybrid Precoding in Heterogeneous Networks with Limited Wireless Backhaul Capacity,Proceedings of the IEEE Global Communications Conference, Abu Dhabi, UAE, 9-13 Dec 2018
Institute of Electrical and Electronics Engineers (IEEE)
This paper investigates a two-tier heterogeneous
networks (HetNets) with wireless backhaul, where millimeter
wave (mmWave) frequency is adopted at the macro base station
(MBS), and the cellular frequency is considered at small cell
BS (SBS) with orthogonal frequency division multiple access
(OFDMA). Subarray structure based hybrid analog/digital precoding
scheme is investigated to reduce the hardware cost
and energy consumption. Our goal is to maximize the energy
efficiency (EE) of the HetNets with limited wireless backhaul
capacity and all users? quality of service (QoS) constraints.
The formulated problem is non-convex mixed integer nonlinear
fraction programming (MINLFP), which is non-trivial to solve
directly. In order to circumvent this issue, we propose a two-loop
iterative resource allocation algorithm. Specifically, we transform
the outer-loop problem into a difference of convex programming
(DCP) by employing integer relaxation and Dinkelback method.
In addition, the first-order approximation is considered to linearize
this inner-loop DCP problem into a convex optimization
framework. Lagrange dual method is adapted to achieve the
optimal closed-form power allocation. Furthermore, we analyze
the convergence of the proposed iterative algorithm. Numerical
results are presented to demonstrate our proposed schemes.
In this paper, we investigate the hybrid precoding
design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS).
Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied.
For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.
Low latency and energy efficiency are two important
performance requirements in various fifth-generation (5G) wire-less networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity
of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively
solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
Abstract?Millimeter wave (mmWave) communication is a
promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Diýerent from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users
are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is diýcult to directly solve the formulated
problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.
In this letter, we study the beamforming design in a lens-antenna array-based joint multicast-unicast millimeter wave massive MIMO system, where the simultaneous wireless information and power transfer at users is considered. First, we develop a beam selection scheme based on the structure of the lens-antenna array and then, the zero forcing precoding is adopted to cancel the inter-unicast interference among users. Next, we formulate a sum rate maximization problem by jointly optimizing the unicast power, multicast beamforming and power splitting ratio. Meanwhile, the maximum transmit power constraint for the base station and the minimum harvested energy for each user are imposed. By employing the successive convex approximation technique, we transform the original optimization problem into a convex one, and propose an iterative algorithm to solve it. Finally, simulation results are conducted to verify the effectiveness of the proposed schemes.
Xiao Lixia, Xiao Pei, Xu Chao, Hemadeh Ibrahim A., Mi De, Hao Wanming (2019) Generalized Space Time Block Coded Spatial Modulation Systems,Proceedings of the 2019 Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2019)
Institute of Electrical and Electronics Engineers (IEEE)
In this paper, Generalized Space-Time
Block Coded Spatial Modulation (GSTBC-SM) is proposed
for Multiple-Input and Multiple-Output (MIMO)
system, which can be extended into an arbitrary
even number of Transmit Antennas (TAs). The proposed
GSTBC-SM scheme employs the hybrid concepts
of Generalized Space-Time Block Coding (GSTBC)
and Spatial Modulation (SM) to further exploit
the diversity benefits of GSTBC using sparse Radio
Frequency (RF) chains. To be more specific, the information
bits are divided into Nu groups and each
group is modulated by SM scheme. Finally, the Nu
symbols are invoked for GSTBC structure. In order to
demonstrated the advantages of our proposed GSTBCSM
schemes, the theoretical Average Bit Error Probability
(ABEP) of our proposed GSTBC-SM is derived.
Both our analytical and simulation results demonstrated
that the proposed GSTBC-SM scheme is capable
of providing considerable performance gains over the
corresponding GSTBC schemes at the same transmit
rate associated with the same number of RF chains.
In this paper, we investigate the energy-efficient hybrid
precoding design for integrated multicast-unicast millimeter
wave (mmWave) system, where the simultaneous wireless information
and power transform is considered at receivers. We adopt two
sparse radio frequency chain antenna structures at the base station
(BS), i.e., fully-connected and subarray structures, and design
the codebook-based analog precoding according to the different
structures. Then, we formulate a joint digital multicast, unicast
precoding and power splitting ratio optimization problem to
maximize the energy efficiency of the system, while the maximum
transmit power at the BS and minimum harvested energy at
receivers are considered. Due to its difficulty to directly solve
the formulated problem, we equivalently transform the fractional
objective function into a subtractive form one and propose a
two-loop iterative algorithm to solve it. For the outer loop, the
classic Bi-section iterative algorithm is applied. For the inner
loop, we transform the formulated problem into a convex one
by successive convex approximation techniques and propose an
iterative algorithm to solve it. Meanwhile, to reduce the complexity
of the inner loop, we develop a zero forcing (ZF) technique-based
low complexity iterative algorithm. Specifically, the ZF technique
is applied to cancel the inter-unicast interference and the first
order Taylor approximation is used for the convexification of the
non-convex constraints in the original problem. Finally, simulation
results are provided to compare the performance of the proposed
algorithms under different schemes.
In this paper, we propose intelligent reflecting surface (IRS) aided multi-antenna physical layer security. We present a power efficient scheme to design the secure transmit power allocation and the surface reflecting phase shift. It aims to minimize the transmit power subject to the secrecy rate constraint at the legitimate user. Due to the non-convex nature of the formulated problem, we propose an alternative optimization algorithm and the semidefinite programming (SDP) relaxation to deal with this issue. Also, the closed-form expression of the optimal secure beamformer is derived. Finally, simulation results are presented to validate the proposed algorithm, which highlights the performance gains of the IRS to improve the secure transmission.
In this paper, we consider an edge cache-assisted millimeter
wave cloud radio access network (C-RAN). Each remote
radio head (RRH) in the C-RAN has a local cache, which can prefetch
and store the files requested by the actuators. Multiple RRHs
form a cluster to cooperatively serve the actuators, which acquire
their required files either from the local caches or from the central
processor via multicast fronthaul links. For such a scenario, we
formulate a beamforming design problem to minimize the secure
transmission delay under transmit power constraint of each RRH.
Due to the diffculty of directly solving the formulated problem, we
divide it into two independent ones: i) minimizing the fronthaul
transmission delay by jointly optimizing the transmit and receive
beamforming; ii) minimizing the maximum access transmission
delay by jointly designing cooperative beamforming among RRHs.
An alternatively iterative algorithm is proposed to solve the
first optimization problem. For the latter, we first design the
analog beamforming based on the channel state information of the
actuators. Then, with the aid of successive convex approximation
and S -procedure techniques, a semidefinite program (SDP) is
formulated, and an iterative algorithm is proposed through SDP
relaxation. Finally, simulation results are provided to verify the
performance of the proposed schemes.
In this paper, we investigate the downlink secure
beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject
both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semidefinite
programming (SDP) relaxation are applied to transform it
into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore,
a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect
CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated
techniques, we design an efficient iterative algorithm
to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.
This paper exploits a generic downlink symbiotic radio (SR) system, where a Base Station (BS) establishes a direct (primary) link with a receiver having an integrated backscatter device (BD). In order to accurately measure the backscatter link, the backscattered signal packets are designed to have ?nite block length. As such, the backscatter link in this SR system employs the ?nite block-length channel codes. According to different types of the backscatter symbol period and transmission rate, we investigate the non-cooperative and cooperative SR (i.e., NSR and CSR) systems, and derive their average achievable rate of the direct and backscatter links, respectively. We formulate two optimization problems, i.e., transmit power minimization and energy ef?ciency maximization. Due to the non-convex property of these formulated optimization problems, the semide?nite programming (SDP) relaxation and the successive convex approximation (SCA) are considered to design the transmit beamforming vector. Moreover, a low-complexity transmit beamforming structure is constructed to reduce the computational complexity of the SDP relaxed solution. Finally, the simulation results are demonstrated to validate the proposed schemes.